Introduction

Configuration management is a critical component in IT operations, enabling organizations to maintain control over their infrastructure, ensure consistency, and improve scalability. In 2026, configuration management tools are evolving to support more complex environments, including cloud computing, microservices, and hybrid infrastructures. These tools automate the process of managing configurations across distributed systems, ensuring that they are consistent, compliant, and secure.

Choosing the right configuration management tool is crucial for IT teams and DevOps engineers to streamline workflows, increase efficiency, and reduce human error. In this blog post, we’ll explore the top 10 configuration management tools available in 2026, covering their features, pros, cons, and how they can be used to support different organizations and project needs.

Top 10 Configuration Management Tools in 2026

1. Ansible

Short Description:
Ansible is an open-source configuration management tool that automates tasks such as application deployment, configuration management, and IT orchestration. It’s popular for its simplicity and ease of use, particularly for smaller teams or organizations just starting with automation.

Key Features:

• Agentless architecture (does not require installation on managed nodes)
• Simple YAML-based playbooks
• Extensible with roles and modules
• Integrates easily with existing infrastructure (cloud, on-prem)
• Secure communication over SSH

Pros:

• User-friendly and easy to learn
• Lightweight and fast execution
• Minimal setup and no agent required

Cons:

• Lacks some advanced features compared to other tools
• Limited GUI options for users who prefer visual interfaces
• Can struggle with larger infrastructures

2. Puppet

Short Description:
Puppet is one of the most widely used configuration management tools in enterprise environments. It focuses on automation, configuration enforcement, and ensuring consistency across environments.

Key Features:

• Declarative language for defining configurations
• Large ecosystem of pre-built modules
• Role-based access control (RBAC) for security
• Integration with other tools like Jenkins, Kubernetes, and AWS
• Reporting and auditing capabilities

Pros:

• Strong community and enterprise support
• Scalable for large environments
• Extensive ecosystem of modules

Cons:

• Steeper learning curve for beginners
• Requires agents on managed nodes
• Can be resource-intensive for smaller environments

3. Chef

Short Description:
Chef is an automation platform for managing infrastructure at scale. It uses a code-driven approach, which allows for greater flexibility and control over the configuration of systems.

Key Features:

• Configuration written in Ruby-based domain-specific language (DSL)
• Strong version control with Chef server
• Built-in reporting and auditing tools
• Integration with cloud platforms and DevOps tools
• Supports both Linux and Windows environments

Pros:

• Highly customizable for advanced users
• Powerful for large-scale environments
• Strong version control and reporting

Cons:

• Ruby DSL may not be ideal for all users
• Requires more complex setup
• Steep learning curve

4. SaltStack

Short Description:
SaltStack is a powerful configuration management tool known for its speed and scalability. It uses a declarative approach and is particularly useful for managing large and distributed infrastructures.

Key Features:

• High-speed, real-time configuration management
• Agent-based and agentless modes
• YAML-based configuration files
• Automated security and compliance management
• Scalable architecture for large environments

Pros:

• Fast and efficient for large environments
• Scalable and flexible
• Real-time automation

Cons:

• Complex setup and configuration
• Learning curve for new users
• Limited community resources compared to Ansible and Puppet

5. Terraform

Short Description:
Terraform is a tool for automating the provisioning and management of infrastructure, especially in cloud environments. It’s ideal for managing infrastructure as code and integrates well with cloud platforms such as AWS, Azure, and Google Cloud.

Key Features:

• Infrastructure as code (IaC) for cloud and on-prem environments
• Works across multiple cloud providers
• Declarative configuration
• Modular structure for reusability
• State management and version control for infrastructure

Pros:

• Excellent for cloud infrastructure management
• Multi-cloud and multi-provider support
• Declarative configuration files for easier management

Cons:

• Limited in traditional on-prem infrastructure management
• Complex state management for large environments
• Requires careful planning and organization

6. CFEngine

Short Description:
CFEngine is an open-source configuration management tool that provides automated IT infrastructure management. It’s particularly suited for large-scale enterprises requiring robust, scalable automation.

Key Features:

• Scalable to handle thousands of nodes
• Lightweight agent for minimal resource usage
• High-performance with fast execution times
• Strong compliance and reporting capabilities
• Designed for both cloud and on-prem environments

Pros:

• Lightweight and fast execution
• Strong security features and compliance checks
• Excellent scalability

Cons:

• Smaller community and fewer modules compared to Puppet or Ansible
• More complex setup and management
• Less user-friendly interface

7. Red Hat Ansible Automation Platform

Short Description:
Red Hat Ansible Automation Platform is an enterprise version of Ansible, providing additional tools and features for managing large-scale automation projects with enterprise-level support.

Key Features:

• Includes Ansible Tower for GUI-based management
• Workflow automation for multi-step processes
• Advanced security features
• Enhanced reporting and analytics
• Integration with existing enterprise tools

Pros:

• Ideal for enterprises with large-scale automation needs
• Provides excellent support and training
• Powerful GUI with Ansible Tower

Cons:

• Premium pricing for enterprise features
• Requires knowledge of both Ansible and Ansible Tower
• Can be complex for smaller teams

8. Puppet Bolt

Short Description:
Puppet Bolt is a lightweight orchestration tool for automating tasks without needing an agent or infrastructure. It’s best for ad-hoc automation and running scripts on remote servers.

Key Features:

• No agents or daemons required
• Supports both Linux and Windows
• Runs over SSH or WinRM
• Modular extensions and plugins
• Easily integrates with other tools

Pros:

• Simple and fast for ad-hoc tasks
• No setup required for remote nodes
• Easy to use for small teams and projects

Cons:

• Limited scope for full infrastructure management
• Lacks some advanced features compared to other tools
• May not scale well for large environments

9. Juju

Short Description:
Juju is a configuration management and application orchestration tool developed by Canonical, the company behind Ubuntu. It’s particularly useful for managing complex applications and services.

Key Features:

• Focus on application modeling and orchestration
• Works across public and private clouds
• Easily integrates with existing systems
• Modular and customizable
• Strong multi-cloud support

Pros:

• Excellent for application-focused automation
• Great for cloud-native applications
• Easy to model and deploy complex applications

Cons:

• Limited community support compared to other tools
• Primarily geared toward Ubuntu and Linux environments
• More suited for developers than system administrators

10. Bcfg2

Short Description:
Bcfg2 is an open-source configuration management tool designed to automate the management of configuration files across a distributed environment. It focuses on ensuring systems are compliant with the desired configuration.

Key Features:

• Client-server architecture for managing configurations
• Supports custom configurations and templates
• Strong focus on compliance and reporting
• Designed for high performance in large environments
• Can handle both software and hardware configurations

Pros:

• Strong compliance and security features
• Customizable and extensible
• Ideal for large-scale environments

Cons:

• Smaller community compared to Ansible or Puppet
• Requires agents on managed nodes
• Less user-friendly than some alternatives

Comparison Table

Which Configuration Management Tool is Right for You?

Decision-Making Guide:

• Small to Medium Enterprises: If you have a smaller infrastructure, Ansible or Puppet Bolt might be the best fit due to their simplicity and scalability.
• Large Enterprises: For complex, multi-cloud environments, consider Chef, Puppet, or SaltStack. These tools provide robust scalability and advanced features.
• Cloud-Centric Organizations: Terraform is ideal for companies managing cloud infrastructures, especially multi-cloud setups.
• Application-Centric Teams: Juju is perfect if you’re focusing on cloud-native applications and need orchestration tools.
• Security and Compliance Needs: If your primary concern is compliance, Bcfg2 offers strong reporting and compliance features.

Conclusion

Configuration management tools are critical for ensuring infrastructure consistency, automation, and scalability in 2026. With an evolving landscape, these tools have adapted to handle cloud environments, microservices, and complex hybrid infrastructures. By choosing the right tool based on your company’s size, infrastructure needs, and budget, you can streamline IT operations, improve efficiency, and reduce human error.

Try out demos or free trials to see which configuration management tool works best for your needs. The right tool can save you time, effort, and resources while ensuring the smooth running of your IT infrastructure.

SEO Section

Meta Description:
Explore the top 10 configuration management tools in 2026, including features, pros, cons, and comparison to help you choose the best solution for your infrastructure needs.

FAQs:

1. What is configuration management?
   Configuration management is the process of automating the management of IT infrastructure and software configurations, ensuring consistency and reducing errors.
2. Which configuration management tool is best for small businesses?
   Ansible and Puppet Bolt are great choices for small businesses due to their simplicity and lightweight design.
3. Can I use these tools for cloud environments?
   Yes, tools like Terraform and SaltStack are specifically designed for managing cloud infrastructures.
4. Are these tools free?
   Many tools like Ansible and Terraform offer free versions, though enterprise features may come with a paid license.
5. How do I choose the right configuration management tool?
   Consider your infrastructure size, the need for automation, ease of use, and your team’s expertise when selecting a tool.

Chef has revamped its channel approach, rolling out a new partner program that addresses inconsistencies in the company’s previous partnering efforts; more IT channel news.

Chef, a DevOps tool provider, has launched a new channel program that the company said provides a uniform structure for working with its nearly 500 Chef partners, which include systems integrators, managed service providers and resellers.

Chef, based in Seattle, offers four tools covering various facets of DevOps and DevSecOps. Chef Infra automates infrastructure configuration, Chef InSpec automates security and compliance, Chef Habitat automates application lifecycle management and Chef Automate provides a DevOps dashboard for operational visibility. Chef partners typically use the tools on digital transformation projects in which they help customers adopt modern software development practices.

Chef had been offering Chef Infra, InSpec and Habitat on an open source basis, but in 2019 extended open source licensing to Chef Automate, which originated as a propriety product. The previously mixed licensing model contributed to an uneven partnering approach, which Chef is looking to remedy with its revised program.

“We had no consistent way of interacting with these partners,” said Vikram Ghosh, vice president of business development at Chef.

The previous channel effort relied heavily on partner managers. But when a manager moved to a different job within Chef or left the company, the relationship with the partner would “go sideways,” Ghosh said.

The larger issue, however, was a conflict with service-oriented Chef partners who saw value in the vendor’s open source tools but were asked to sell the Chef Automate observational layer. The revised partner program, which builds upon the company’s 100% open source approach, aims to align Chef’s business model with partner needs, Ghosh said.

Components of the Chef Partner Program include a co-sell incentive model for partners who don’t wish to pursue product transactions as part of their service offerings. The program also includes support for the recurring revenue model and co-marketing opportunities as well as sales/technical enablement programs and tools.

Chef’s channel program offers three partner tiers: Principal, Senior and Junior. Top-level Principal partners are those that deliver “great customer outcomes” with Chef, differentiate themselves through integration with Chef’s technology partners and take advantage of Chef’s go-to-market programs, Ghosh said. Chef’s technology partners include Cloudbees, HashiCorp and Jenkins.

Channel partners play a number of roles in the Chef ecosystem. For one, they provide resources for delivering and supporting Chef technology. “From our standpoint, we are a relatively small company,” Ghosh said. “We need the scale that our partners provide.”

Systems integrator partners help customers integrate Chef’s offerings into the broader DevOps tool chain and CI/CD pipeline, he noted. MSPs, for their part, help customers manage the system an integrator sets up. “Customers don’t want to manage the underlying system that is delivering a business outcome,” Ghosh said.

The need for DevOps managed services has contributed to an SI-to-MSP shift, he added.

The Chef Partner Program in the coming months plans to highlight partner success stories through its marketing channels, including blog posts, events and webinars. Some of those stories, Ghosh said, will focus on SIs that found ways to provide ongoing services that drive recurring revenue.

Digital transformation projects, DR and marketing spell channel work
A channel partner to-do list might include restarting stalled digital transformation projects, boosting customer confidence in disaster recovery, and investing in sales and marketing — based on the results of industry surveys released this week.

Forty-two percent of the 293 U.S.-based IT decision-makers polled in a survey sponsored by Ahead, an enterprise cloud solutions provider based in Chicago, said they struggle to achieve success in digital transformation. The most-cited stumbling blocks are legacy systems and technical debt, according to the survey, which Hanover Research conducted on Ahead’s behalf. Overall, 67% of respondents identified infrastructure as a big part of their digital transformation strategies.

A US Signal survey, meanwhile, revealed three-quarters of the 110 U.S.-based IT decision makers it polled now use backup and disaster recovery from a third-party edge data center provider. That said, a significant portion of the respondents were not completely at ease with the business continuity offerings they’ve purchased. Fifty-four percent said they are somewhat or not very confident in their DR and backup solutions in the event of cyberattacks, outages or other incidents.

The survey from US Signal, a data center services provider, based in Grand Rapids, Mich., also noted 66% of respondents reported operating a hybrid IT infrastructure, while 20% said they were entirely in the cloud and 14% said they were entirely on-premises.

Beyond infrastructure issues, the MSP portion of the channel continues to grapple with sales and marketing. The North American MSPs surveyed in a SolarWinds report listed sales, lack of resources and marketing as their biggest growth obstacles. The results are consistent with other service provider surveys that have also cited sales and marketing as an MSP challenge.

The SolarWinds survey, conducted in conjunction with The 2112 Group, also pointed to patterns in MSP security. North American respondents said they were most comfortable offering and using antivirus, firewalls, data backup and recovery, and endpoint security. They were the least comfortable with biometrics, cloud access security brokers and digital rights management, according to the report.

Offshore players continue cloud consultancy purchases
Infosys, a $12 billion digital services and consulting firm based in Bengaluru, India, agreed to acquire Simplus, a Salesforce partner with headquarters in Salt Lake City.

The $250-million transaction is expected to close in the next 30 days, according to a Simplus spokesperson. Simplus offers cloud consulting, implementation, data integration, change management and training services for Salesforce CPQ and the Salesforce Billing add-on package. Simplus has offices in North America, Australia, the United Kingdom, and operates a delivery center in Manila.

The pending deal closely follows Atos’ acquisition of Maven Wave, a Google Cloud partner based in Chicago. Atos, a Paris-based IT services company, said the acquisition will bolster its cloud services footprint in North America. The Infosys-Simplus transaction is also reminiscent of Wipro Ltd.’s acquisition of Appirio, a Salesforce consulting firm, Appirio in 2016.

Other news
IBM Storage updated and consolidated its Storewize and FlashSystem products under the IBM FlashSystem brand, a move that was largely shaped by partner input, the vendor said. A company spokesperson said close to 150 partners and distributors globally gave feedback on the design and performance of the FlashSystem offering. In a blog post, Eric Herzog, IBM’s chief marketing officer and vice president of worldwide storage channels, noted the FlashSystem launch doesn’t affect IBM Storage’s go-to-market strategy. FlashSystem models 5000, 5100 and 7200 will continue to be offered exclusively through IBM business partners, he said.
Nerdio, a Microsoft Azure automation platform company that targets MSPs, closed $80 million in funding. The company said it would use the funding to drive product development and support for Microsoft Azure offerings, including Windows Virtual Desktop, as well as build out operations and resources for its channel partners. According to a Nerdio spokesperson, Nerdio expanded aggressively in the channel through 2019 by striking distributor agreements with Pax8 and Ingram Micro and pushing into international markets, such as Latin America, Belgium and the Netherlands.
TPx Communications, a Los Angeles-based MSP, has been acquired by affiliates of private equity firm Siris Capital Group. In conjunction with the acquisition, TPx said, its CEO, Richard A. Jalkut, will retire, and Siris executive partner Joseph A. Cozzolino will serve as executive chairman of the board and interim CEO.
US Signal tapped Infinidat, a data storage company based in Waltham, Mass., to provide storage arrays for a Detroit area data center scheduled to open in March 2020. US Signal already uses 12 Infinidat InfiniBox systems in seven data centers in the Midwest.
In the managed detection and response sector, eSentire Inc., based in Waterloo, Ont., launched esCLOUD, which extends its on-premises MDR offerings to cloud environments. In addition, Agio, a managed security services provider based in New York, unveiled a partnership with Respond Software. Agio is incorporating Respond Software’s technology into its MDR services.
Pax8, a cloud distribution company based in Denver, formed a partnership with Novacoast, an IT services and solutions provider. Under the arrangement, Pax8 will offer Novacoast’s novaSOC, a monitoring and alerting security service.
Atera, an IT automation platform based in New York, launched Atera Network Discovery, a tool the company said scans networks and extracts a list of business opportunities and tasks for MSPs.
Unified Office Inc., an MSP based in Nashua, N.H., developed an offering for the hospitality industry, based on its Total Connect Now business communication product. The hospitality offering will integrate with hotel management and messaging services such as Opera, Synergy and Kipsu, according to the company.
Atos is forming a systems integrator relationship with RingCentral. The companies plan to launch a co-branded unified communications as a service
Netsurion, a managed security service provider (MSSP), integrated its co-managed SIEM offering, EventTracker, with the Mitre ATT&CK cybersecurity framework. The ATTT&CK framework is a public knowledge base of threat tactics and techniques developed by Mitre Corp., a nonprofit research and development organization. Netsuiron said the integration will be available in the upcoming 9.3 release of EventTracker.
High Wire Networks, an MSSP, launched Overwatch 24/7, a new security platform for managed service providers. The platform integrates detection and response technology with High Wire Network’s security operations center as a service, the company said.
DLT Solutions, a technology aggregator and Tech Data subsidiary based in Herndon, Va., added offerings from Glasswall, Illumio, NetBrain, Parasoft, Perfect Sense, TYCHON and ZorroSign to its product roster.
Apps Associates, an MSP and Oracle cloud migration specialist, rolled out a diagnostic and management toolset, dubbed Apps Pack.
Extreme Networks signed a 10-year agreement with Gannon University to establish a regional networking training center for students and professionals. Additionally, Gannon and Extreme will integrate Extreme’s training curriculum and technical certification into the academic curriculum of Gannon’s undergraduate and graduate engineering and computing programs, the vendor said. An Extreme spokesperson noted that partners in the region will have access the facility, dubbed Extreme Academy, for on-site training. Under the partnership, Extreme will work alongside Networking Technologies, a value-added reseller (VAR) and integration, to outfit and upgrade equipment for the Extreme Academy and other Gannon academic buildings.
Axonius, a cybersecurity asset management platform vendor, appointed Mark Daggett as its vice president of channels and alliances. Dagget joins Axonius from Cylance, where he served as director of global distribution.
MSP360, a data backup and recovery solutions provider based in Lewes, Del., appointed Kate Knyazheva as head of inside sales. She joins the company from Veeam.
Matrix Integration, an IT solutions provider based in Jasper, Ind., appointed Rob Wildman as vice president of professional services.

Background: Monzo’s Infrastructure at Scale

Monzo is a mobile-only digital bank that runs its core infrastructure on AWS. Its backend is built on Kubernetes, with Apache Cassandra as the primary database, Apache Kafka for messaging, and Go for most application code. At the time this case study was shared, Monzo operated approximately 1,500 microservices with more than 9,300 inter-service interactions.

Managing network access at that scale is non-trivial. The Monzo security engineering team set zero-trust networking as a core goal: no service—inside or outside the cluster—should be trusted by default. Instead, each service should be permitted to communicate only with a pre-approved list of other services.

What Zero-Trust Networking Means in a Kubernetes Context

Kubernetes, by default, allows unrestricted communication between all pods in a cluster. Zero-trust networking inverts this: all traffic is denied unless explicitly permitted. Achieving this in a cluster with hundreds or thousands of services requires a disciplined, automated approach to policy management—manual configuration does not scale.

Kubernetes provides a native NetworkPolicy resource for this purpose, but its feature set has limits. Monzo used the Calico CNI (Container Network Interface) plugin, which extends standard Kubernetes network policies with additional capabilities—such as logging traffic that would have been dropped, without actually dropping it. This logging-only mode was critical to the team’s rollout strategy.

Discovering Inter-Service Dependencies with Static Analysis

Before enforcing any network policy, the team needed to understand which services actually talk to each other. They built a custom tool called rpcmap, which discovers inter-service dependencies through static code analysis of the Go codebase.

The team explicitly chose static analysis over two alternatives:

• Integration test observation: Monzo’s codebase is large and not every code path has an integration test. Testing-time observations would produce an incomplete dependency map.
• Runtime observation: Just because a service call is rare does not mean it never happens. As Backend Engineer Jack Kleeman noted, a bank can have yearly processes—a service dependency exercised once a year would be missed by short-window runtime sampling.

Static analysis produces a deterministic, complete picture of intended call paths, making it the most reliable foundation for policy generation at this scale.

Initial Approach: Testing on a Single Service

Rather than rolling out policies cluster-wide immediately, the team isolated a single service first. This pilot revealed several early limitations:

• The initial design placed the burden of maintaining the allowed-caller list on the destination service team, creating an ownership and coordination problem.
• Dev teams had to edit Kubernetes config files by hand—error-prone and difficult to review.
• Policy testability was weak; it was hard to verify that policies behaved correctly before enforcing them.

Using Calico’s Extended Policy Features for Safe Rollout

To address testability, the team engaged with the Calico community. They discovered that Calico offers features beyond what standard Kubernetes NetworkPolicy exposes—specifically, the ability to log traffic that would be denied without actually dropping it.

The rollout strategy was:

1. Configure the enforcement policy to run at the lowest priority (last in the evaluation chain).
2. Enable audit/log mode so that traffic which would be blocked is logged instead of dropped.
3. Monitor network traffic to identify which services would have had packets dropped under full enforcement.
4. Adjust the policy rules based on findings before switching to hard enforcement.

This approach allowed the team to validate their generated policies against real cluster traffic with zero disruption to production services.

Shifting Policy Ownership to the Invoking Service

One of the most important design decisions was flipping who owns the allowed-access list. Rather than making the destination service responsible for declaring who may call it, the team made the calling (invoking) service responsible for declaring which services it needs access to.

This was expressed through Kubernetes labels. Each service used labels in the network policy’s ingress spec to declare its required downstream dependencies. This has a key advantage: the team that owns a service also owns its dependency declarations, which aligns responsibility with knowledge.

Handling High-Fan-Out Services with Service-Type Grouping

Some services—such as monitoring or observability tooling—call a large number of other services. Listing every individual dependency for such services would be unmanageable. To handle this, services were grouped by a service-type label, and access policies were applied to the group rather than individual service names. This kept policies readable and maintainable even for high-fan-out scenarios.

Automating Policy Generation in the Deployment Pipeline

With the design validated, the team integrated rpcmap into the CI/CD pipeline. It runs on every commit, statically analysing the code to produce dependency rule files. The deployment pipeline then converts these rule files into the Kubernetes/Calico service labels and network policy objects automatically.

This eliminates manual Kubernetes config editing and ensures that network policies stay in sync with the code as it evolves.

Future Direction: Moving to a Service Mesh

The team acknowledged that enforcing access control at the CNI layer has limitations—primarily that it operates at the IP/port level rather than at the application (L7) level. Monzo indicated plans to move this enforcement to a service mesh layer, and noted they had already begun migrating to a custom mesh based on Envoy. A service mesh allows richer, identity-aware policy enforcement, mutual TLS between services, and more granular observability than CNI-layer policies alone.

Key Takeaways for Platform and Security Engineers

• Default-deny is achievable at scale, but requires automated policy generation—manual approaches break down past a few dozen services.
• Static code analysis is a more reliable source of dependency truth than runtime observation when code paths include infrequent but critical processes.
• Use audit/log mode before enforcement to validate policies against real traffic without risking production outages.
• Assign policy ownership to the calling service, not the destination, to align responsibility with the team that has the most context.
• Group services by type to manage high-fan-out dependencies without bloating individual policy definitions.
• CNI-layer network policies are a starting point; a service mesh provides deeper, L7-aware enforcement as a natural next step.

Monzo’s approach demonstrates that zero-trust networking inside a Kubernetes cluster is operationally feasible at significant scale, provided it is backed by automation, static analysis, and a phased rollout strategy.

Why Quantitative Valuation Metrics Matter for Investors

Quantitative valuation frameworks give investors a systematic, repeatable way to assess whether a stock is cheap or expensive relative to its fundamentals. Rather than relying on intuition or a single ratio such as price-to-earnings, these composite models aggregate multiple signals to reduce the noise any one metric can introduce. This article explains the most widely used frameworks — Value Composite, Q.i. Value, Piotroski F-Score, Beneish M-Score, Magic Formula Rank, and ERP5 — using Splunk Inc. (NasdaqGS: SPLK) as a concrete reference point from 2019.

Value Composite Scores (VC1 and VC2)

Developed by quantitative investor James O’Shaughnessy, the Value Composite framework ranks stocks by combining several valuation ratios into a single score between 1 and 100. A score near 0 suggests a stock may be undervalued; a score near 100 suggests it may be overvalued relative to peers.

VC2: Six-Ratio Composite

The Value Composite 2 (VC2) incorporates the following six ratios:

• Price to Earnings (P/E) — measures how much investors pay per dollar of earnings.
• Price to Cash Flow (P/CF) — compares share price to operating cash flow per share.
• EBITDA to Enterprise Value (EV/EBITDA) — a capital-structure-neutral profitability measure.
• Price to Book Value (P/B) — compares market price to net asset value.
• Price to Sales (P/S) — useful for companies with volatile or negative earnings.
• Shareholder Yield — combines dividend yield and net share buyback yield.

At the time of the original report (September 2019), Splunk Inc. had a VC2 score of 82, indicating the stock appeared richly valued on a composite basis relative to the broader market universe being ranked.

VC1: Five-Ratio Composite

Value Composite 1 (VC1) removes shareholder yield from the calculation, leaving the five price-based ratios. For SPLK at the same date, VC1 stood at 81 — essentially consistent with VC2, suggesting that shareholder yield was not a meaningful differentiator in this case.

Q.i. Value: Identifying Undervaluation via Four Ratios

The Q.i. Value ranking uses four ratios focused on yield and liquidity:

• EBITDA Yield — EBITDA divided by enterprise value.
• Free Cash Flow (FCF) Yield — FCF divided by enterprise value or market cap.
• Earnings Yield — earnings per share divided by share price (inverse of P/E).
• Liquidity — a measure of how easily the company can meet short-term obligations.

The lower the Q.i. Value score, the more undervalued the company is considered to be. Splunk’s Q.i. Value of 58 in September 2019 placed it in a middling range — neither strongly undervalued nor severely overvalued on this particular set of yield-focused measures.

Piotroski F-Score: Balance Sheet Health

The Piotroski F-Score, developed by accounting professor Joseph Piotroski, assesses the financial strength of a company using nine binary tests derived from the financial statements. Each test that a company passes awards one point, for a maximum score of 9.

The nine criteria span three categories:

• Profitability: positive return on assets, positive operating cash flow, improving ROA, cash flow exceeding net income (accruals quality).
• Leverage and Liquidity: declining long-term debt ratio, improving current ratio, no new share dilution.
• Operating Efficiency: improving gross margin, improving asset turnover ratio.

Interpretation guidelines:

• 8–9: Strong financial position.
• 3–7: Moderate — further analysis warranted.
• 0–2: Weak — potential red flags.

Splunk’s F-Score of 5 at the time placed it in the middle range, suggesting a mixed but not alarming financial picture. For a high-growth SaaS company investing heavily in sales and R&D, a mid-range F-Score is not unusual.

Beneish M-Score: Detecting Earnings Manipulation

The Beneish M-Score, developed by Professor Messod Beneish, is a probabilistic model designed to flag whether a company may be manipulating its reported earnings. It uses eight financial ratios derived from the income statement and balance sheet — examining things like changes in receivables, gross margin, asset quality, sales growth, and accruals.

The critical threshold is -2.22:

• An M-Score greater than -2.22 raises a flag for possible manipulation.
• An M-Score below -2.22 suggests manipulation is less likely.

Splunk’s M-Score of -2.70 fell below the -2.22 threshold, indicating that the model did not flag earnings manipulation as a significant concern at that point in time. Investors should treat this as one signal among many rather than a definitive verdict.

Gross Margin Score: Stability and Growth Over Eight Years

This score, sometimes referred to as the Gross Margin (Marx) score, evaluates the stability and growth of a company’s gross margin over the trailing eight years. It is ranked on a scale of 1 to 100, where 1 is positive (consistent, growing gross margins) and 100 is negative (volatile or declining margins).

Splunk’s Gross Margin score of 32 suggested relatively stable gross margin performance over the measured period — a reasonable result for a software company that was transitioning to a subscription model and maintaining high gross margins typical of SaaS businesses.

Magic Formula Rank (MF Rank): Quality at a Fair Price

The Magic Formula was developed by hedge fund manager Joel Greenblatt and described in his book The Little Book That Beats the Market. The formula ranks companies using two ratios:

• Return on Invested Capital (ROIC) — measures how efficiently the company generates profit from capital.
• Earnings Yield — EBIT divided by enterprise value, indicating how cheap the stock is relative to its operating earnings.

Companies are ranked separately on each metric and the ranks are combined. The lower the combined MF Rank, the more attractive the stock is considered. Splunk’s MF Rank of 12,917 — out of a universe that can include tens of thousands of stocks — indicated that SPLK did not rank as an obvious Magic Formula pick at that time, which aligns with its high VC2 score.

ERP5 Rank: A Multi-Factor Value and Quality Screen

The ERP5 Rank extends the Magic Formula concept by adding two more dimensions:

• Earnings Yield
• ROIC
• Price to Book Value
• 5-Year Average ROIC — smooths out single-year anomalies in capital efficiency.

As with MF Rank, a lower ERP5 Rank is generally considered better. Splunk’s ERP5 Rank of 15,247 reinforced the picture painted by the other metrics: a high-quality business by some measures, but not a screaming bargain on valuation in late 2019.

Price Index Data: Momentum Signals

The price index is a simple momentum indicator calculated by dividing the current share price by the share price at a fixed point in the past. A value above 1.0 indicates price appreciation; below 1.0 indicates a price decline.

Splunk’s price index readings as of September 2019:

The longer-term indices showed strong multi-year momentum, while shorter-term readings suggested the stock had pulled back in the months immediately preceding the report — a common pattern in high-growth technology names.

Historical Volatility

Volatility figures measure the annualised standard deviation of price returns over a given window. Higher volatility means wider price swings — relevant for position sizing and risk management.

Splunk’s volatility readings as of September 2019:

• 3-Month Volatility: 36.55%
• 6-Month Volatility: 37.33%
• 12-Month Volatility: 48.07%

The declining volatility from the 12-month to the 3-month window suggests that SPLK’s price action had become somewhat more stable in the near term relative to the prior year — though all three figures remained well above typical large-cap averages, consistent with a high-growth SaaS stock.

Putting It All Together: How to Use These Metrics

No single metric tells the whole story. Combining multiple frameworks helps build a more complete picture:

• Use VC1/VC2 to establish whether a stock looks cheap or expensive on traditional valuation ratios.
• Use the Piotroski F-Score to assess whether the underlying business is improving or deteriorating financially.
• Use the Beneish M-Score as a quality-control check on the reliability of reported earnings.
• Use MF Rank and ERP5 Rank to compare a stock’s value-and-quality profile against a broad universe of peers.
• Use price index data to understand momentum context and whether recent price action aligns with the longer-term trend.
• Use volatility data to calibrate position size and set realistic expectations for price swings.

In Splunk’s case, the 2019 snapshot painted the picture of a high-quality, high-growth technology company that was not cheap by traditional valuation standards — a characterisation consistent with how the market was pricing cloud software businesses at that time.

This article is for educational purposes only and does not constitute investment advice. All figures cited are historical data points from approximately September 2019 and do not reflect current market conditions.

Understanding “Olde World” IT: Why DevOps Exists

Understanding where DevOps came from is key to understanding what it actually is. To appreciate the movement, it helps to look at what IT operations looked like in the late 1990s and through most of the 2000s.

In large enterprises of that era, adding compute capacity meant placing multi-hundred-thousand-dollar hardware orders — servers, networking gear, cables, and software licenses — destined for on-site and off-site data centers. VMware was still convincing skeptical CFOs that virtual machines were cost-effective. Many companies ran applications directly on physical hardware. Entire teams were dedicated to data center operations: negotiating lease rates, managing cooling, and ensuring offshore night-shift staff could identify the right server rack without pulling the wrong cable during after-hours trading.

Software releases were similarly painful. The typical workflow looked something like this:

• Business analysts gathered requirements in meetings developers were not invited to attend.
• Developers wrote code to those requirements, optionally writing unit tests along the way.
• When code was marked “Ready for QA,” a quality assurance engineer picked it up and ran it in their own environment — which might bear little resemblance to production or even the developer’s local setup.
• Failures were sent back to developers days or weeks later, depending on business priorities.

Both sysadmins and developers shared one common frustration: change management. Most enterprises followed ITIL practices, which imposed heavily regulated processes governing when and how technical changes could happen. The intent was to minimize costly mistakes. In practice, this meant releases only every two weeks and all server changes — regardless of size or impact — batched into a Friday-evening-to-Monday-morning maintenance window. Ironically, this batching of work often led to even larger, more complex failures when things went wrong.

DevOps Is Not a Tiger Team

One of the most persistent and damaging misconceptions about DevOps is that it is a team, a department, or a Jira project. You may have encountered this in practice: a “DevOps team” that owns the CI/CD pipeline, a “configuration management group” that has to approve your deployment, or release planning meetings scheduled weeks after code was marked complete. If that sounds familiar, you are watching history repeat itself — the same silos, just with new labels.

Silos form naturally. People gravitate toward colleagues who share their context and vocabulary. In software companies, as codebases grow in complexity, developers working on related features cluster together. What starts as informal collaboration becomes official team structures. That is entirely human. The problem arises when those structures harden into walls, and the walls generate friction, blame, and waste.

In the pre-DevOps world, developers and sysadmins not only worked in separate silos — they actively competed. Developers blamed sysadmins for broken or overly locked-down environments. Sysadmins blamed developers for making arbitrary changes that destabilized shared systems and demanding more compute than they actually needed. Neither side understood the other’s constraints, and neither side was particularly motivated to learn. Developers threw environment problems over the wall; sysadmins prioritized them among hundreds of other requests; everyone waited angrily.

DevOps is a way of thinking, not an org chart entry. The movement’s core premise is that developers, sysadmins, and business stakeholders should function as a unified team. They do not need to know everything about each other’s domains, but they need to know enough to understand each other’s work, read each other’s backlogs, and speak a shared language. This is the philosophical foundation behind infrastructure as code, shared deployment pipelines, and accessible monitoring dashboards — practices that make the entire system legible to everyone involved.

As Chef co-founder Adam Jacob put it: “DevOps is the word we will use to describe the operational side of the transition to enterprises being software led.”

What Do You Actually Need to Know to Get Into DevOps?

The honest answer is: it depends on the company. The “DevOps engineer” role varies significantly by organization size, maturity, and existing team composition.

• Smaller companies with many developers but limited infrastructure expertise tend to hire people with strong systems administration backgrounds who can also write code.
• Larger or older organizations with established sysadmin teams often look for something closer to what Google calls a Site Reliability Engineer — essentially a software engineer who designs and improves operational functions.

Regardless of company size, the following skill areas appear consistently in DevOps roles:

Cloud Platforms

Knowing how to architect and administer secure, scalable cloud environments is table stakes. AWS is the most common platform to encounter, but Azure, Google Cloud Platform, and managed PaaS providers like DigitalOcean and Heroku are also widely used. Focus on understanding core networking concepts (VPCs, subnets, security groups), IAM and access controls, compute and storage services, and how to design for resilience.

CI/CD Pipelines

Continuous integration and continuous delivery are the mechanical heart of DevOps practice. Understanding how to build, optimize, and troubleshoot deployment pipelines is essential. Common tools include Jenkins, GoCD, Travis CI, CircleCI, and increasingly GitHub Actions and GitLab CI. Learn not just how to configure these tools but why specific pipeline strategies — blue/green deployments, canary releases, feature flags — exist and what problems they solve.

Monitoring, Logging, and Alerting

You cannot operate what you cannot observe. Familiarity with time-series metrics tools (Grafana, Prometheus), log aggregation platforms (the Elastic Stack including Kibana and Logstash, Splunk, Loggly), and alerting pipelines is expected. The goal is not just to collect data but to derive actionable signals from it.

Infrastructure as Code

Manually configured infrastructure is a liability: it is hard to reproduce, hard to audit, and hard to recover from. Configuration management tools like Ansible, Chef, and Puppet allow you to define system state in version-controlled code. Provisioning tools like Terraform and AWS CloudFormation let you declare entire infrastructure topologies the same way. This discipline is central to DevOps because it makes infrastructure legible, testable, and collaborative.

Containers and Orchestration

Containers have become a standard unit of deployment. Docker is the dominant container runtime, with alternatives like containerd now widely used under the hood. Kubernetes has emerged as the standard orchestration platform for running containers at scale, handling scheduling, self-healing, scaling, and service discovery. Understanding how to build, publish, and run container images, and how to write basic Kubernetes manifests, will serve you well in almost any DevOps role.

Skill Gaps: What Sysadmins and Developers Each Need to Learn

If You’re Coming from Systems Administration

You will need to learn to write code — not just shell scripts, but real programming. Python and Ruby are the most practical starting points for this path. Both are portable across operating systems, readable, and widely used in the DevOps ecosystem. Python underpins Ansible and is the dominant language for AWS, Azure, and GCP SDKs. Ruby is the foundation of Chef and Puppet. Either language will open doors; Python has somewhat broader adoption today.

Focus on writing clean, testable automation scripts before attempting to build complex tooling. The ability to write a reliable Python script that interacts with a cloud API or parses logs is immediately valuable in a DevOps role.

If You’re Coming from Software Development

Invest time in Unix fundamentals, basic Windows Server administration, and networking. Even though cloud platforms abstract away much of the complexity of raw infrastructure, debugging slow application performance, network latency, or mysterious crashes is dramatically easier when you understand what happens below the application layer. Learn how processes, file descriptors, TCP/IP, DNS, and HTTP actually work. This knowledge pays dividends every time something breaks in production — which it will.

Hands-On Projects to Build Your Skills

Reading about DevOps is a starting point; doing it is how you actually learn. Here are practical ways to build experience:

Deploy a Real Application Through a Full Pipeline

Take an open-source application from a public GitHub repository and build everything required to run it in production: a cloud environment provisioned with Terraform or CloudFormation, a CI/CD pipeline that runs tests and deploys on merge, and monitoring that alerts on errors and latency. Doing this end-to-end — even for a toy application — teaches you more than any course because you will encounter real failure modes.

Replicate a Manual Setup with Infrastructure as Code

Choose a common service — a web server, a database cluster, a message queue — and set it up manually on a cloud provider first. Document every step. Then tear it down and rebuild the entire thing using only Terraform (or CloudFormation) and Ansible. This two-pass approach forces you to understand what you are automating before you automate it, and the resulting code is something you can show to potential employers.

Run Containers Locally, Then in the Cloud

Start by containerizing an existing application using Docker. Run it locally with Docker Compose to simulate a multi-service environment. Then push the images to a container registry and deploy them to a managed Kubernetes service (Amazon EKS, Google GKE, or Azure AKS all offer free-tier or low-cost options for learning). Working through the networking, storage, and configuration challenges of a real Kubernetes deployment will sharpen your skills quickly.

Final Thoughts

Getting into DevOps is not about memorizing a tool list. It is about developing a mindset that treats infrastructure as software, values collaboration over hand-offs, and prioritizes feedback loops that make systems safer and faster to change. The tools matter, but they exist in service of that philosophy.

Start with the fundamentals — cloud basics, scripting, and a CI/CD pipeline — and build outward from there. Pick one real project, make it actually work in production, and you will have learned more in that process than in months of passive study.

This article draws on content originally published at opensource.com, adapted and expanded for a DevOps engineering audience.

How the Game Is Changing

At its core, DevOps is the practice of automating and accelerating the software development lifecycle, embodying the principles of agile methodology through tooling, culture, and process. The goal is to let developers respond to business needs with minimal latency — removing the bottlenecks that have historically slowed software delivery.

Cloud computing amplifies that goal in several concrete ways:

• Centralized, standardized infrastructure. Cloud platforms provide a consistent environment for testing, staging, and production. This solves the fragmentation problem of traditional distributed enterprise systems, where heterogeneous hardware and software configurations made centralized deployment pipelines difficult to maintain.
• Native DevOps tooling. Major public and private cloud providers now build continuous integration (CI) and continuous delivery (CD) capabilities directly into their platforms. This reduces the cost and complexity of maintaining on-premises DevOps toolchains, while also providing centralized governance — a practical benefit for teams that struggle to enforce process consistency across departments.
• Usage-based cost accounting. Cloud environments track resource consumption at a granular level — by application, developer, or environment — making it straightforward to attribute development costs and adjust allocation dynamically. Traditional on-premises infrastructure typically lacks this visibility.

An important distinction worth noting: cloud is not driving DevOps adoption — DevOps is driving cloud adoption. Teams that commit to DevOps practices quickly discover that on-premises infrastructure introduces its own latency: procurement cycles, hardware provisioning, and capacity planning all slow down pipelines that are otherwise highly automated. Cloud removes those constraints.

Why DevOps Is Leading Teams to the Cloud

Many enterprises moved away from waterfall development toward agile and DevOps specifically because application development backlogs were stalling business initiatives — product launches, acquisitions, integrations. The expectation was that agile practices alone would solve the problem.

In practice, that expectation falls short. Even a highly automated DevOps pipeline runs into walls when the underlying infrastructure requires capital expenditure cycles. Developers may complete work rapidly only to wait weeks for hardware to be provisioned before deployment can happen. Cloud infrastructure removes that constraint by making compute, storage, and networking available on demand.

This creates a genuine mutual dependency: DevOps without cloud still carries infrastructure latency, while cloud without DevOps lacks the automation and process discipline needed to realize its full potential. Organizations that treat these as independent initiatives tend to underdeliver on both.

Approaching Cloud Application Development

Successfully combining DevOps with cloud development requires changes that start at the engineering level, not just at the executive level. Engineers who understand the tools, processes, and feedback loops involved are the ones who will identify problems early and drive corrections. Below is a practical sequence for teams beginning or maturing this journey.

1. Define Your Development Requirements

Audit your current development process honestly. Identify where latency exists — in build, test, approval, or deployment stages — and map what the future state needs to look like. This baseline is essential before selecting any tooling or platform.

2. Build the Business Case

Investment in DevOps tooling and cloud migration requires organizational buy-in. Document the expected return on investment — faster release cycles, reduced infrastructure overhead, lower defect rates — in terms that resonate with financial and operational stakeholders.

3. Define Your Initial DevOps Processes

Design your CI/CD pipeline, branching strategy, testing approach, and release process. Treat these as living documents — they will evolve as your team learns what works. Plan for iteration rather than perfection in the first pass.

4. Align DevOps Tooling With Your Cloud Platform

DevOps tool selection cannot happen in isolation from platform selection. Your pipeline, automation, monitoring, and deployment tooling must integrate cleanly with the cloud environment where applications will run. Evaluate the native DevOps services offered by your chosen cloud provider alongside third-party tools, and ensure they work together cohesively. This is where many organizations stumble — selecting tools independently of platform capabilities and then struggling with integration gaps.

5. Address the People Challenge

Adopting DevOps and cloud simultaneously is a significant cognitive and cultural shift. Training helps, but leadership matters more. Senior engineers and technical leads need to model the new ways of working, set clear expectations, and provide guidance as teams encounter unfamiliar tooling and workflows. Traditional developers who are willing to learn but lack direction are often the largest source of friction during this transition.

6. Define CloudOps: Operations as Part of Development

In a DevOps model, developers share responsibility for how their applications behave in production. Cloud platforms provide observability tooling — logging, metrics, distributed tracing — that gives development teams direct visibility into runtime behavior. This feedback loop is valuable: production signals should flow directly back into development priorities. The old model of developers handing off code to a separate operations team and losing visibility ends here.

DevOps Will Lead the Way

As DevOps and cloud prove their combined value, engineering leaders are increasingly working to remove the organizational and technical barriers that slow adoption. The resistance often comes from teams that associate continuous deployment with instability or lower quality — a concern that is understandable but misplaced when proper automated testing, feature flagging, and rollback mechanisms are in place.

The value propositions of DevOps and cloud are genuinely synergistic: fast, automated pipelines need elastic infrastructure to realize their speed advantage, and elastic infrastructure needs automated pipelines to be managed efficiently at scale. Organizations that pursue one without the other will find that the missing piece limits their returns.

The challenge is the simultaneity of the change. Teams must adopt DevOps tooling, shift their culture, align with a cloud platform, and develop operational practices — often all at once, and without a period of stability in which to consolidate what they have learned. That is difficult, but it is the nature of the transition.

Getting Past the Friction

The cultural dimension of this change is as significant as the technical one. DevOps requires developers to think beyond writing code — to own build pipelines, monitor deployed applications, and treat operational feedback as part of their work. Cloud requires infrastructure thinking to become part of the development conversation. Both shifts take time and deliberate practice.

The investment — in tooling, training, and process redesign — may not yield an immediate, measurable ROI. That is a legitimate concern for finance and leadership stakeholders. The risk of inaction, however, is more concrete: competitors who do make this transition can ship faster, respond to users more quickly, and operate more cost-effectively. In markets where time-to-market matters, that is a compounding advantage.

For engineering teams that have not yet begun, the path forward is to start with a defined scope — a single application, a single team — and prove the model before scaling it. Attempting to transform an entire organization’s development practice simultaneously is a reliable way to overwhelm teams and produce backlash. Start small, learn fast, and expand what works.

This article is an independent technical explanation of the DevOps-cloud relationship. It was inspired by a post originally referencing content from TechBeacon (techbeacon.com). Readers are encouraged to consult current cloud provider documentation and DevOps research for up-to-date guidance.

This article breaks down who is likely to resist a DevOps adoption, why they resist, and the practical steps leadership can take to address each group’s concerns.

What DevOps Implementation Actually Demands

Before addressing resistance, it helps to be honest about what DevOps asks of an organisation. The benefits are real—faster infrastructure provisioning, reliable CI/CD pipelines, continuous testing that catches defects earlier, and better alignment between test and production environments. But reaching those benefits requires genuine change across multiple teams:

• Process changes: Waterfall or siloed handoff models give way to continuous collaboration between development, QA, and operations.
• Tooling changes: Teams must adopt containerisation (e.g., Docker), infrastructure as code (IaC), CI/CD pipelines (e.g., Jenkins, GitLab CI), version control practices, and cloud platforms.
• Cultural changes: Shared ownership of delivery outcomes replaces the classic “throw it over the wall” mentality.

Every one of these changes disrupts existing comfort zones. The stakeholders most affected each have their own legitimate concerns—understanding those concerns is the first step to resolving them.

Resistance from Project Managers

Why Project Managers Resist

Project managers (PMs) are typically the first to feel the impact of a DevOps transition. Their resistance tends to stem from an expanded scope of responsibility rather than simple reluctance to change:

• They must now actively champion DevOps to development, QA, and operations teams—a persuasion role they may not have played before.
• They are expected to facilitate continuous collaboration across teams that previously worked in separate phases.
• They need working familiarity with DevOps tooling—CI/CD pipelines, continuous monitoring, version control systems—and with cloud platforms such as AWS, Azure, or Google Cloud.

How to Overcome It

The CIO or transformation sponsor should invest in structured DevOps training for PMs before the rollout begins. This training should cover:

• Core DevOps principles and the business case behind the transition.
• Practical exposure to the tools the teams will use (containerisation, IaC, CI/CD).
• Cross-functional team management—specifically how to coordinate development, QA, and operations working in parallel rather than in sequence.

A well-prepared PM becomes a DevOps evangelist rather than an obstacle. Because PMs interact with every other stakeholder group, their buy-in has a multiplying effect on overall adoption.

Resistance from Developers

Why Developers Resist

Developers frequently resist DevOps not because they oppose automation or faster releases, but because the model changes their day-to-day responsibilities in uncomfortable ways:

• They are now expected to engage with infrastructure concerns—deployments, environment configuration, on-call incidents—that previously belonged to operations.
• They must coordinate their technology choices with operations and QA rather than selecting tools that suit their preferences alone.
• Closer collaboration with less experienced infrastructure staff can feel like a drag on productivity.

How to Overcome It

PMs must apply genuine facilitation skills to build mutual respect between developers and infrastructure engineers. Concrete steps include:

• Ensuring infrastructure engineers receive training on the specific tools the development team uses—Docker, Jenkins, Git workflows, and so on—so developers are not carrying colleagues who cannot keep up.
• Framing DevOps as removing friction for developers (fewer environment mismatches, faster feedback loops) rather than adding responsibility.
• Establishing clear ownership boundaries so developers understand precisely which operational concerns are theirs and which remain with the operations team.

Resistance from QA Teams

Why QA Teams Resist

QA engineers often feel squeezed by DevOps. A faster SDLC means less calendar time for manual testing, and the shift to continuous testing requires skills many test engineers do not yet have:

• Automated functional and regression testing frameworks (e.g., Selenium, Appium).
• API and performance testing tools (e.g., JMeter, Postman).
• Integration of test execution into CI/CD pipelines so that tests run automatically on every code commit.

The fear is understandable: asking a manual tester to own automated test suites overnight is a significant professional leap.

How to Overcome It

QA managers and PMs should work together to reframe the transition as a skills upgrade, not a threat. Practical tactics:

• Provide structured training in test automation and API testing tools before the team is expected to use them in production pipelines.
• Highlight the professional benefit: test engineers who can write and maintain automated test suites are more marketable and command broader responsibilities than manual-only testers.
• Introduce automation incrementally—starting with a subset of high-value regression tests—rather than replacing all manual testing at once.

Resistance from Infrastructure Department Managers

Why Infrastructure Managers Resist

Before DevOps, the infrastructure department manager controlled the design, configuration, and delivery of environments for development, test, and production. Infrastructure as Code (IaC) and self-service provisioning models can make it feel as though that control is being handed to developers—which is a genuine identity challenge for someone whose authority has long rested on gatekeeping infrastructure.

At the same time, the role actually expands: the infrastructure manager must now run their department as a competency centre, ensuring that every project team has the right mix of qualified engineers.

How to Overcome It

The CIO must communicate the new role clearly and early. Key messages:

• The infrastructure manager is not losing control—they are shifting from hands-on gatekeeping to strategic oversight and quality assurance of how infrastructure is provisioned and governed across projects.
• Their new mandate is to proactively identify and resolve skills gaps within project teams before those gaps cause delays, rather than reacting after problems emerge.
• They become an enabler of speed rather than a bottleneck, which is a more influential position in a DevOps organisation.

Resistance from Operations Engineers

Why Operations Engineers Resist

Operations engineers are accustomed to working at the end of the delivery pipeline—receiving a finished build and deploying it. DevOps pulls them into the process from day one, requiring:

• Continuous coordination with developers and QA throughout the project, not just at release time.
• Regular reporting to the PM on task status—a level of visibility that can feel like micromanagement to those not used to it.
• Fluency in automation and version control tooling: configuration management tools (Chef, Puppet, Ansible), version control systems (Git), and deployment automation platforms.
• A new obligation to communicate infrastructure status to both business and technical stakeholders.

How to Overcome It

The infrastructure department manager should own the technical upskilling of the operations team, delivering or arranging training on the specific tools the organisation has adopted. The PM should focus on the cultural side—building genuine working relationships between operations, development, and QA so that cross-team collaboration feels productive rather than intrusive.

It also helps to give operations engineers a voice in tooling decisions early. When engineers help choose the tools they will use, adoption resistance drops considerably.

Resistance from End Users

Why End Users Resist

It may seem counterintuitive that the people who benefit most from faster, more reliable software updates would push back. But end users often raise two specific objections:

• Less time for acceptance testing: A compressed SDLC leaves a shorter window for user acceptance testing (UAT), which can feel like their concerns are being rushed past.
• Distrust of automated testing: End users who have historically relied on their own exploratory testing during UAT cycles may not trust that automated test suites cover the scenarios that matter to them.

How to Overcome It

PMs should maintain consistent, transparent communication with end users throughout the delivery cycle. Specific actions that build confidence:

• Show end users which critical workflows are covered by automated tests and explain the coverage strategy.
• Share test results and defect metrics openly so end users can see that quality gates are functioning.
• Involve end users in defining acceptance criteria that feed directly into automated test cases, giving them genuine ownership over what gets tested.

A Unified Approach: What the CIO Must Drive

Addressing resistance stakeholder by stakeholder requires a coordinated strategy from the top. The CIO or executive sponsor should:

• Communicate openly and early. Every affected team should understand what is changing, why it is changing, and what their new responsibilities will look like—before the transition begins, not during it.
• Invest in targeted training. Generic “DevOps awareness” sessions rarely move the needle. Training should be role-specific: PMs need cross-functional management skills; QA engineers need test automation tools; operations engineers need IaC and configuration management; developers may need exposure to deployment and monitoring practices.
• Listen to concerns. Resistance is often a signal that a legitimate problem has not been addressed. Creating feedback channels—retrospectives, open Q&A sessions, anonymous surveys—helps surface issues before they become blockers.
• Measure and share progress. Demonstrating early wins (faster deployments, reduced incident rates, shorter feedback loops) builds momentum and makes the value of the transition concrete for everyone involved.

Summary

DevOps resistance is not irrational—it reflects real concerns about changing roles, new skill requirements, and unfamiliar ways of working. Each stakeholder group has a different source of friction:

Overcoming this resistance is as much a leadership and communication challenge as a technical one. Organisations that address the human dimension of DevOps adoption with the same rigour they apply to tooling selection consistently achieve faster, more durable transformations.

Securing DevOps environments is an increasingly important concern for chief information security officers (CISOs) and security teams. While developers often recognize security is important, it is not their top priority. More typically, the DevOps team prioritizes delivering new capabilities and features to the business and customers, often as part of a larger digital transformation initiative. And, developers often view security as something that will slow down deployments.

In this episode, host Jon Collins speaks with Christina Noren about her experience running Agile organizations and how DevOps can be implemented at various stages in an organizations life.

Christina has more than 20 years of experience in product, services and operations at successful, venture-backed companies, such as Splunk, Zuora, Portal and Sonic Solutions. She has a particular strength in identifying and refining product offerings in evolving markets. At Splunk, she was employee #12 in 2005 and remained on the senior leadership team through its 2012 IPO. She played a critical role in bringing Splunk’s 1.0 product to market, overseeing requirements definition, product delivery and the beta program. She went on to guide key technical innovations, including search-time knowledge and the Splunk search language. Christina is a co-inventor on the Splunk patent for Machine Data Web.

Transcript

Jon Collins: Hello and welcome to this week’s edition of Voices in DevOps. I’m here to speak to Christina Noren of CloudBees. Now, am I going to get your title right? What do I say?

Christina Noren: I’m the chief product officer, the CPO, and the key thing is that for us as CPO, I’m responsible for all of engineering and product management and product marketing and design and documentation. So I run a whole software organization and we’re building a product to bring a whole software organization together, so I am our internal… first customer of how do we do we do Agile and DevOps and continuous delivery better; and how do we run a software organization in as professional a way, as my peer running our field organization runs his.

Thank you very much for joining us Christina. What we’re trying to get out of this is: organizations out there are struggling with DevOps. They may have started on the DevOps route and they’re finding it more complicated than they thought. So I’m really, really interested already in the fact that you’re not only kind of a purveyor of DevOps products if you like, but you’re also the internal person who makes this stuff happen and therefore you’ve got to ‘walk the talk’ essentially.

Yeah. Exactly. That’s the term we use.

Good. How did you get here? If I may ask, what enabled you to end up being the CPO of one of the world’s foremost… (I mean I don’t want to blow smoke but essentially CloudBees, everyone’s using Jenkins) everyone in the cloud space has heard of CloudBees, so how did you get here?

Yeah well it’s been a funny journey.So CloudBees, you know I’ve been here just a just over a year and my team is responsible for product management, engineering, product marketing, design, documentation as a holistic function in the company. And I’ve been in software for 25 years. Like a lot of people, especially women of my generation, I came in through the back door and got technical [experience] on the job and held a whole variety of roles in my early career.

But starting in the early 2000s, I started running product management organizations just as engineering organizations were starting to move towards Agile, and I ran product for a few different companies in the log management space, but most notably I ran the product, I was the founding VP of product management and also started a lot of other surrounding functions for Splunk, from stealth mode when I was employee #12 in 2005 through our IPO in 2012. In that experience and previous jobs and jobs since, I’ve really found myself trying to figure out how product management and product marketing, the business, support, everything can align with engineering practices that are moving away from waterfall and into Agile; and then being responsible for product at Splunk, as really revolutionary way of looking at what was happening in production.

The sys admins using Splunk were often the ones that started becoming SREs and a lot of DevOps started to happen amongst our customers in those mid-2000s, so the products that I was responsible for… we’re a big part of the Ops side of DevOps. So and then in the most recent CPO job I had at a company called Interana, prior to CloudBees, I really saw how implementing CICD completely transformed the rhythms between product management and design and engineering and our outcomes.

So I was kind of primed when CloudBees came calling and why a historically product leader is running a product engineering organization that’s building a product primarily used by engineering organizations… that was my first objection. But what they really want is a product mindset, and moving from technology and sales driven to being product driven is something that not only a lot of historically technology companies are doing, but is actually one of the things that companies with traditional IT organizations are doing. They’re trying to become product organizations and that’s the core of becoming software companies.

Well I’m literally trying to draw pictures as you speak. I’ve got a mesh is appearing, we’ve got the Ops side, we’ve got the engineering side, we’ve got the product side. And then we’ve got the Agile thing and I think what’s really interesting about what you’re saying there is: seeing Agile from an Ops perspective and from a product perspective. And then how that feeds back into development, that’s pretty much the reverse of what other people are talking about.

Yeah. And you can find a slide show that’s still up (from 2008) under my name of [me] talking about automating product management in an Agile world, which was kind of my fledgling stage of making peace with engineering doing Agile, which was an early stage of development. But these days you know we really talk about ‘continuous’ and not just continuous integration, continuous delivery, its technical practices, but I’ve been talking a lot about ‘continuous everything’ as a way of looking at every process in the business that touches software delivery, or that software delivery touches, and realizing that if it doesn’t change to be compatible with continuous approaches, it’s going to be an impedance mismatch that robs the organization of much of the value of continuous software delivery.

So take me back to 2008 if you will, and I’m wondering about which came first: the chicken or the egg on this one? Is it that the product mindset works better in an Agile way or is it that the Agile ‘continuous everything’ mindset enables better products?

Well I think it’s both, but I think… Agile came out of engineers revolting against how ineffective, you know the waterfall methodologies were. And a lot of very simplistic views of what a product manager is doing is maintaining the roadmap, and it’s damn hard to maintain a roadmap when engineering is iterating and changing sprint to sprint.

I think today’s generation of product managers have really embraced Agile, even if they’re still quixotically trying to produce roadmaps. There’s a real disconnect there. But I think in the day to day work, good product managers, good young product managers who aren’t tainted by the early bad experiences, like they really see their job as supporting that voice of the customer, taking early iterations quite rapidly to customers, a learning mindset, an experimental mindset and so forth.

So now I think Agile is very much at the heart of what a healthy contemporary product mindset is about. But you know as a second time young BPO product manager I was still in this, “Oh my God, my board members expect and my customers, my enterprise customers expect these roadmaps and if they change, it’s a big deal and how the hell do I produce these roadmaps?” Engineering is just all over the place doing this Agile thing. Right? That was my very early like “Oh my God I’ve got to make peace with Agile as a product manager, and what’s expected of me is a mismatch with what engineering is now delivering.”

You were stuck right in the middle of a dilemma. And I’m thinking—correct me if I’m wrong and I’m going to ask about 17 questions all at the same time. I’ll try and distill it down as best I can. But essentially you’re talking about the world of product if you like, so that’s the vendors, the software creation from a vendor perspective space.

So one of the questions is around how does that map into what you’re seeing with your customers? But another part of it is: a lot of the conversations around DevOps that I’m having and hearing and so on, certainly the more traditional ones, it’s about CICD. So it’s right in the middle of the development cycle as opposed to where product management fits, which is overarching. What are we going to do next? et cetera et cetera. So there’s all these questions milling around in my head.

Yes. So let me jump into that because the interesting thing is that most, and some other analysts have been writing about this for a few years or just like the rise of product management in I.T., but so if every company is becoming a software company, then IT departments are becoming software development organizations and whether the titles change or not, the mindset is changing and those who are ‘getting it’ are actually realizing that even if you’re an insurance provider or a financial services company or supply chain or whatever, the idea that IT is serving the business with projects is giving way to a product mindset. And the difference being that a product is a long running, ever evolving, thing that is being provided to a market segment, and we’re seeing that product mindset.

We’re seeing IT organizations that are really committed to their Agile and DevOps transformations and the cultural part are hiring or turning people into product managers, and they’re creating product teams whether or not the CIO’s title changes or IT changes as an department/organization name, but they are changing themselves into being product companies whether or not the consumers of those products appear to be their internal users. But increasingly because of mobile and apps and self-service and products like cars that are more vehicles for software than anything else.

Increasingly their end customers are consuming a software product from them. So this is to me very relevant and it’s not just for a narrow set of ISVs like ourselves that are producing enterprise software for a living and some consumer tech companies that have software product managers and software products. Every company that is believing that it is becoming a software company in their industry rather than that industry type company where an IT department is serving them, that’s the heart of these transformations.

And companies that are just thinking, to go to the second part of your question, that DevOps is just a technology and process adoption of CICD for the build-test-deploy lifecycle; and even more, early stage, just CI for build, test, get your artifacts, and then there’s some separate system to serve Ops PNs [or] whatever, deploying that to a complex development environment. If they see it as just that, they’re not going to see the full benefit.

But you know it’s a good starting place, and it often leads to these kinds of realizations because once you start doing that, then you start realizing that there’s not just other problems but there’s other opportunities. And if the team starts getting infected with DevOps culture and Agile culture from the ground up, they start saying “We’re no longer IT serving the business, we are empowered cross-functional teams delivering products that are constantly growing and getting better and serving our customers better over time.” And that’s really the engine of innovation and value in companies today.

So my experience of enterprise software delivery is a bit stale. It’s a couple of decades gone and largely it’s through the usual analytical research that I keep up to date, but I’m not doing it anymore. Back in the day when I was within this kind of thing, there were requirements teams, and they tended to have every requirement under the sun.

So what I was doing as the precursor of Agile and XP, a DSM consultant, was going in and saying you only need 60 use cases, you don’t need 500 requirements in this massive catalog. But so they were doing the kind of product thing, but they weren’t phrasing it in that way. And they were doing it wrong as far as Agile was concerned.

I mean 20+ years ago I was in MSN operations at Microsoft, carved out from Microsoft corporate IT to run our online services. And I produced requirements documents just like that and I managed teams of consultants from Deloitte and Andersen Consulting before it was Accenture. You know there were requirements analysts producing these things and nothing was ever on time. When it was delivered, it was never anything that was actually useful. So yeah I’m reformed from that stage too.

It’s okay, I’ve sat through the counseling now. Hi I’m John Collins, and I’ve done requirements management… So fast forward to the present day and how does it translate into, I’ll call it ‘the modern enterprise,’ which could be an oxymoron for some organizations. But when you are working with organizations today that, as you say, every enterprise should be an ISV, how are they actually managing their product orientation, their requirements, how would they frame this?

They’re all in transition. And you know I haven’t met a lot of leaders in IT or technology in companies in any industry that are at that level or realization of what they need to do with DevOps or CICD that when I sit down with them that I can’t have the conversation we’re having now and that they’re not already working on the cultural change. Their understanding of how far beyond engineering, beyond you know software delivery functions that extends varies. But this is coming from our customers’ experience of trying to get you out of Agile and DevOps to continuous practices, and expectations in the marketplace of becoming software or technology companies and understanding what that means.

And then what we’re also seeing is a lot of cross pollination where traditional companies in traditional industries, their IT teams are starting to staff people that have worked previously in the digital native companies: the Facebooks, the Googles, whatever, and they’re coming into more traditional companies. You’re seeing that more traditional banks are hiring people out of like a Capital One that understands themselves [more] as a technology company that happens to be in banking and it’s really good. And so you’re seeing the cross pollination, you’re seeing these ideas come in. You’re seeing people you know that are in the process of reform from what we just described, embrace these new ways.

There’s so much maturity around these new ways, and then you start to see sometimes individual pockets of teams that just sort of revolt or do their own thing and start working this way and start learning from outside and showing their organizations the results of a different way of doing things. So it’s changing. I think there’s differences in how much visibility is at like a C-level leadership outside of the CIO, of what the implications are for the business as a whole. Sometimes it’s a new CEO coming in because a board and the overall company sees they need to make this transformation and they’re fired up and driving it. Sometimes it’s within the IT organization, CIO organization that these new practices are coming and the CIO is explaining to their peers what the meaning of all this change is and why it benefits the organization.

I’d really like to pick you up in a positive way on the fact that you said the process of reform i.e. it is this kind of an evolutionary adaptation to the new world. And then you also used the term ‘transformation.’ And my disdain of the term ‘digital transformation’ is [from] the fact that it’s a bit like “we can get you a six pack in six weeks or beach body” or whatever. It’s all about this kind of ‘top down’ notion that all you’re going to do is just change and everything will be brilliant. Whereas if we do sit more in the process of reform kind of way that makes a lot more sense to me.

Yeah I mean it has to become grassroots whether or not it starts that way. You have to start having these pockets of people who want to do things in a different way. And start spreading that message. And it needs sponsorship and support from an executive level.

We do these CloudBees Days and we just had one in Madrid and in Zurich and San Francisco and Dallas and various places, and there’s a number of them and our customers that present tend to be the leaders of, (I know you hate the term) the “digital transformation” efforts or DevOps transformation efforts in their respective companies. They’re usually at a mid level of management where they’re close to the people on the ground and they’re responsible directly to people in senior levels and they’re translating between them, and that’s universally what they say on stage. They universally talk about how they have to overcome religious ideas at all levels. They have to overcome a lot of existing processes and so forth. T

The only way it works is when the people who are doing the work start finding their own way, their own flavor of doing this. There’s the executive support to understand that the SLA, if you will, from an organization working this way is going to be different—that the needs of that organization and the outputs of that organization are going to be different. There’s going to be a transition but they need support in terms of budget to invest in the tools, the training, the infrastructure that is new.

So one thing we have to talk about by the way, is just the cloud part of this, which is sometimes… coming in because people want to get the benefits of running the cloud and not running their own infrastructure anymore, and ‘cloud native’ and so forth, That starts to unravel and the people doing application development start thinking about converting legacy applications, microservices, and when you start to get into that culture, you can’t hire a bunch of people to work in that culture who don’t carry with them the seed of Agile and DevOps practices. All these things are sort of mutually supporting in terms of how these things are coming.

Actually to me amazingly, my year in CloudBees like a year ago, talking to the same customer as this year, the rate of change in organizations that we would consider to be very traditional enterprises with very traditional IT departments, it’s just been dramatic in terms of the rate of change and it’s been amazingly organic in the ones that are being very successful and management has let that organic process happen.

And I think you’ve hit on exactly… I don’t have a problem with things changing but I think it was this digital transformation as an epiphany. It’s a kind of overnight flick as soon as you brought in the term ‘organic.’ I’m thinking about it’s a bit like spring. You know it’s that kind of transition from nothing growing to suddenly you start seeing green shoots and everything, and it just kind of starts heading in the right direction because everyone’s getting it, and I’m much more comfortable with it…

I actually use a lot of garden and organic metaphors when I talk about this, but you know again a lot of people who have been speaking at our events talk this way, and if you think your digital transformation has an end date or has ended, you’re not getting it, which is digital transformation is constant, it’s a continuous process. And it’s a process of constantly adapting new tools and practices and culture. And evolving the culture to be able to be more effective and it doesn’t mean you can’t get a small start wherever you want. Start with one small team. Go ahead and start trying to drive nightly continuous delivery to internal servers across a set of projects and see how… and let the behaviors on the teams change as these practices enable them to change.

I’d love to be a fly on the wall of some of those sessions because these are what make the world go ‘round. Picking up on something you said there, it’s the difference between… I hear a lot of organizations need a cultural shift. What you’re actually talking about is enabling that cultural shift, so kind of getting out of the way of a cultural shift that would be inevitable if you just stop trying so hard to make it happen.

And again I’ve learned this the hard way, over and over and over again. So just in terms of some internal projects, we’ve actually been pretty transparent on the blogs and the blogs on CloudBees. We’re going through a digital transformation, we’re learning from it but we’re never going to end that digital transformation. But when I’m trying to drive change, often I end up having to do a reset and instead do different things to get the little seeds of change to flower and grow with at the doer level in the teams.

That’s brilliant. So essentially I think I’m gonna have to start wrapping up,not that I think we couldn’t carry on for a few hours. This is through necessity not through desire, but we started talking about a product mindset, [then] moved on to talking about that kind of leadership approach and then we somehow segued into digital transformation approaches and what’s going to make a real difference to the organization from a groundswell perspective.

So if you were talking to an organization now and you want to kick off some of these conversations and you want to enable some of that stuff to happen, what would be the first things that you advise? How would you start that conversation?

Well I think if it’s… let’s just pretend it’s an organization that is in a traditional industry with a long standing IT function serving the business. Start by asking if I can, the CEO or senior leadership, do you think that you are on a path to becoming… to where software creation is central to your business? And do you want to learn how to get on that path?

It’s one of those questions because they might say, “sure,” and then you’d go out, wouldn’t you?

Well then you start to unpack and then you start to say “OK well you know, what are the practices that the companies in the world that are most successful at delivering software, engage in? What’s the culture of those companies?” and then ”Do you have a part of your business that is maybe most greenfield, maybe you’re starting up a new digital service to replace a very human driven service? And can you staff that with a mix of people who come out of some of these organizations that are natively digital companies and can you cross pollinate that with the people that you find in your organization who are most excited about trying a new way?”

So can you start and get success with a few fresh pockets and learn how to do it, and figure out what are some of the core tools? Obviously, CICD is amongst them but tools like observability, and what does your tech stack look like and figure that out, figure out what is your day to day Agile process? What does DevOps mean? Are you really getting engineers to own production? And with continuous delivery the output of that?

How does the rest of the organization respond and start learning by doing and again get people in that can help coach you? Start but don’t bring in brand new teams from outside because the ultimate goal is that your teams and people transform and learn this new way, so you don’t lose what you have.

Excellent. Well, I’m gonna wrap up here. Thank you so much, Christina. [That was] Christina Noren who is CPO at CloudBees and that’s been highly informative. I know I would say I’ve learned a lot. That whole product mindset starting point has really influenced my thinking today. So thank you so much for that and thanks for joining me.

SQL Server 2008 and 2008 R2 Reach End of Support

On July 9, 2019, Microsoft officially ended mainstream and extended support for SQL Server 2008 and SQL Server 2008 R2. After more than a decade in production environments worldwide, the two database versions stopped receiving security patches, bug fixes, and technical support under Microsoft’s standard lifecycle policy.

John Chirapurath, General Manager for SQL Server and Azure Data + AI at Microsoft, marked the milestone with a blog post thanking customers “for the ten amazing years we’ve had together” while simultaneously directing them toward Microsoft’s Azure-based alternatives.

Migration Options Microsoft Offered

Microsoft laid out three primary paths for customers still running SQL Server 2008 or 2008 R2 at end of support:

• Migrate to Azure SQL Database (managed service): Microsoft positioned this as “evergreen SQL,” arguing that customers moving workloads to its fully managed platform would no longer need to manage patching, version upgrades, or future end-of-support deadlines. Microsoft also cited security improvements and elastic scalability as key benefits of the managed service.
• Rehost on Azure Virtual Machines: Customers who were not ready to move to a fully managed database service could lift and shift their existing SQL Server 2008 or 2008 R2 instances onto Azure Virtual Machines. Microsoft promised this path would require “few or no application code changes” and, critically, would grant an additional three years of free Extended Security Updates (ESUs).
• Purchase Extended Security Updates on-premises: Organizations that needed or wanted to keep their databases on-premises could purchase three additional years of “critical” security updates only. This option was positioned as a last resort, with Microsoft clearly favoring the cloud migration paths.

New Azure SQL Services Announced Alongside the Cutoff

Alongside the end-of-support announcement, Microsoft used the moment to preview new SQL-related services on Azure:

• Unified Azure SQL Management Portal: A simplified portal experience allowing administrators to manage both Azure SQL Database and SQL Server on Azure Virtual Machines from a single interface. This feature entered preview at the time of the announcement.
• SQL Server Big Data Clusters: A preview feature that integrated SQL Server with Apache Spark and Hadoop, enabling organizations to work with relational and non-relational data side by side within a single platform.

Expanded Storage for Azure Database for MySQL and PostgreSQL

In a related announcement, Microsoft also previewed “large storage” tiers for Azure Database for MySQL and Azure Database for PostgreSQL. Both offerings added support for up to 16 TB of storage and up to 20,000 IOPS, targeting high-throughput, large-scale workloads that had previously required more complex infrastructure arrangements.

What This Meant for Database Administrators

For DBAs and DevOps engineers still running SQL Server 2008 environments in mid-2019, the end of support created a genuine security and compliance risk. Without ongoing security patches, any newly discovered vulnerabilities would remain unpatched unless organizations took one of the three paths Microsoft outlined.

The free Extended Security Updates available to Azure VM customers represented a meaningful incentive: teams that could not immediately re-architect their applications gained up to three additional years to plan a proper migration, without sacrificing security updates. On-premises ESU customers faced the same three-year window but had to pay for the privilege and received only critical-severity patches.

For teams evaluating migration options at the time, Microsoft’s messaging made the Azure VM lift-and-shift path the lowest-friction short-term option, while Azure SQL Database represented the longer-term strategic target for workloads that could be adapted to a managed service model.

Key Takeaways

• SQL Server 2008 and 2008 R2 reached end of extended support on July 9, 2019.
• Customers migrating to Azure Virtual Machines received three additional years of free Extended Security Updates.
• On-premises customers could purchase ESUs covering critical patches only for three years.
• Azure SQL Database was promoted as a fully managed, “evergreen” alternative requiring no future upgrade planning.
• Microsoft simultaneously launched previews of a unified SQL management portal, SQL Server Big Data Clusters, and large-storage tiers for Azure Database for MySQL and PostgreSQL.

What Are Productivity Bots?

Productivity bots are software agents—usually integrated into messaging platforms such as Slack or Microsoft Teams—that automate repetitive tasks, collect information, trigger cross-service workflows, or surface analytics without requiring a human to context-switch into a separate application. In a DevOps context, they reduce toil, keep distributed teams aligned, and connect otherwise siloed tools.

Key Productivity Bot Categories and Representative Tools

The market spans several distinct use-case categories. The tools below are representative of each category and are frequently cited together in enterprise evaluations.

Workflow Automation and Integration

• Zapier – A no-code/low-code integration platform that connects thousands of SaaS applications via automated workflows called “Zaps.” DevOps teams use it to chain together event triggers across tools such as GitHub, PagerDuty, Jira, and Slack without writing custom glue code.
• Troops – Focuses on surfacing CRM and sales data (primarily Salesforce) inside Slack, enabling revenue and support teams that interact with engineering to get instant context without leaving their messaging workflow.

Task and Project Management Bots

• Jira (Atlassian) – While Jira itself is a full project-tracking platform, its Slack and Teams bots allow engineers to create issues, transition tickets, and receive deployment or build notifications directly in a chat channel, reducing the need to context-switch into the Jira UI.
• Trello – Trello’s bot and Power-Ups ecosystem lets teams create and update cards from within messaging platforms. It is commonly used by smaller DevOps teams for kanban-style sprint and incident tracking.
• Any.do – A task-management application with assistant features aimed at individual productivity; less commonly used in enterprise DevOps pipelines but relevant for personal workflow management.

Stand-up and Async Meeting Bots

• Geekbot – Automates asynchronous stand-ups, retrospectives, and check-ins directly inside Slack. Engineers answer bot-prompted questions at their own pace; Geekbot aggregates responses into a shared channel. Widely used by remote and distributed DevOps teams to replace synchronous daily meetings.
• Polly – A polling and survey bot for Slack and Teams. DevOps leads use Polly for quick pulse checks after incidents, sprint retrospective voting, and lightweight NPS-style team-health surveys.
• Simple Poll – A lightweight Slack polling tool for quick in-channel votes, such as choosing a deployment window or prioritizing backlog items.

Survey and Feedback Collection

• SurveyMonkey – An established survey platform with Slack integration that allows teams to distribute and collect structured feedback at scale. Useful for post-incident reviews, change advisory board feedback, and developer experience surveys.

AI-Powered Assistants and Scheduling

• Zoom.ai – An AI meeting-scheduling assistant designed to reduce the back-and-forth of calendar coordination. Note: as of the time of writing, Zoom.ai had been acquired by Symantec parent NortonLifeLock; verify current product availability before evaluating.

Analytics and Reporting Bots

• Statsbot – Delivers analytics and business intelligence data (from Google Analytics, Mixpanel, Salesforce, and similar platforms) into Slack channels on a scheduled or on-demand basis. Engineering and product teams use it to monitor KPIs without leaving their primary communication tool.

Key Evaluation Criteria for DevOps Teams

When assessing productivity bots for a DevOps environment, consider the following dimensions:

• Platform compatibility – Does the bot support your messaging platform (Slack, Microsoft Teams, Google Chat) and your CI/CD toolchain?
• Security and data residency – Bots often request broad OAuth scopes. Review what data leaves your environment and whether the vendor meets your compliance requirements (SOC 2, GDPR, etc.).
• Integration depth – A bot that only posts notifications has less value than one that supports bidirectional interaction (e.g., acknowledging an alert, closing a ticket, or merging a PR from within Slack).
• Customization and API access – Can workflows be tailored to your team’s processes, or are you locked into a rigid template?
• Pricing model – Many productivity bots charge per active user per month. Calculate total cost against the time savings they deliver.
• Reliability and uptime – A bot that sits in the critical path of incident response must be highly available. Check vendor SLA documentation.

Common DevOps Use Cases for Productivity Bots

Automated Async Stand-ups

Tools like Geekbot eliminate the need for a fixed daily stand-up call. Engineers respond to three standard questions (what did you do yesterday, what will you do today, any blockers?) at a time that suits their schedule. Responses are posted to a shared channel, giving team leads and other engineers visibility without a synchronous meeting.

Incident Response Coordination

Workflow bots such as Zapier can trigger automated Slack notifications when a monitoring alert fires, create a Jira incident ticket, page the on-call engineer, and open a dedicated Slack channel—all in seconds. This dramatically reduces mean time to acknowledge (MTTA) during outages.

Sprint Retrospectives and Team Health

Polly and Simple Poll allow scrum masters and engineering managers to run lightweight retrospectives and pulse surveys inside Slack. Anonymous response options encourage more candid feedback than in-person sessions.

Cross-Tool Data Visibility

Statsbot and Troops surface data from external platforms into the place where engineers already spend their time. Instead of opening five dashboards during a post-mortem, a team can query key metrics directly from Slack.

Market Landscape Considerations

The productivity bot space is competitive and evolving quickly. Several trends are shaping the market:

• Platform consolidation – Slack and Microsoft Teams are each building native bot and workflow capabilities (Slack Workflow Builder, Teams Power Automate integration) that compete with standalone tools.
• AI integration – Large language model capabilities are being embedded into bots, enabling more natural interaction patterns beyond button clicks and slash commands.
• Remote and distributed work adoption – The growth of remote engineering teams has accelerated demand for async-first tools like Geekbot and Polly.
• Security scrutiny – Enterprise security teams are applying tighter governance to third-party bots that can read message history or access internal data stores.

Getting Started: A Practical Approach

1. Identify your highest-toil workflows – Stand-ups, ticket creation, and deployment notifications are common starting points with measurable time savings.
2. Start with one bot per category – Avoid tool sprawl. Pick one async stand-up tool and one workflow automation tool, measure the impact, then expand.
3. Review OAuth permissions carefully – Limit bot access to the minimum scopes required for the use case.
4. Set a review cadence – Evaluate bot usage data quarterly. Unused bots accumulate cost and security surface area.
5. Engage the team – Bots that engineers find annoying will be muted or ignored. Pilot with a volunteer team before rolling out broadly.

Productivity bots are not a silver bullet, but when matched to genuine workflow friction and adopted thoughtfully, they can meaningfully reduce toil, improve async collaboration, and surface the data DevOps teams need to move faster and more reliably.

What Is Test-Driven Development?

TDD has been part of software engineering discourse since at least the late 1990s, emerging from the extreme programming (XP) movement. American software developer Kent Beck is widely credited with formalizing and popularising TDD around 2003 as a technique for creating simple designs and building developer confidence. Today TDD is a natural companion to agile and DevOps workflows because of its short, rapid feedback loop.

The TDD cycle is often summarised in three steps:

1. Red — Write a test that targets a specific piece of behaviour. Run it. It should fail immediately because the production code does not exist yet.
2. Green — Write the minimum amount of production code needed to make the test pass.
3. Refactor — Clean up the code you just wrote, using the passing test as a safety net to confirm you have not broken anything.

Repeating this cycle continuously drives the design of the software from the outside in, keeping each unit small, focused, and testable.

Key Benefits of TDD

1. Forces a User-Centric Design Mindset

Because a developer must think about test cases before writing implementation code, the focus naturally shifts toward how the software will be used rather than how it will be built internally. This promotes cleaner interfaces and more intentional API design.

2. Provides a Continuous Quality Metric

A suite of TDD tests gives the team an always-up-to-date signal about code health. Running the suite after every change surfaces regressions within seconds rather than days.

3. Acts as Living Documentation

Well-written unit tests describe the intended behaviour of a piece of code in concrete, executable terms. Unlike comments or external documentation, tests stay accurate because a failing test is immediately visible.

4. Enables Safe Refactoring

TDD encourages frequent refactoring as a first-class activity. Because every behaviour is covered by a test, developers can restructure code with confidence, knowing the test suite will catch any unintended breakage. Over time this eliminates duplication, enforces clear object boundaries, and improves overall maintainability.

5. Produces Modular, Extensible Code

Writing tests for small, isolated units of behaviour naturally pushes developers toward loosely coupled, modular designs. Code that is easy to test tends to be easy to extend and reuse, which pays dividends across the full software lifecycle.

Where TDD Fits: Greenfield and Legacy Projects

New (Greenfield) Projects

Applying TDD from day one is straightforward: understand the testing tools available for your technology stack, then follow the red-green-refactor cycle for every new feature. For example, in an Angular project generated with the Angular CLI, a spec file is scaffolded alongside every component automatically. Using the Jasmine testing framework, a TDD workflow for a new Angular component might look like this:

1. Identify a discrete piece of functionality the component should provide.
2. Write a Jasmine unit test targeting that functionality — it will fail immediately.
3. Start the test runner in watch mode so tests re-run on every file save.
4. Write just enough component code to make the failing test pass.
5. Refactor the component code while keeping the test green.

Legacy Projects

Introducing TDD into an existing codebase does not require a big-bang rewrite. A practical starting point is bug fixes:

• When a bug is reported, first write a test that reproduces the failure.
• Confirm the test fails (proving the bug exists).
• Fix the production code until the test passes.
• Commit both the fix and the test together.

After several iterations, the team accumulates a regression suite that prevents old bugs from resurfacing, builds confidence in the codebase, and creates a foundation for broader TDD adoption.

Measuring Testability with Code Coverage

A code coverage tool reports the percentage of production code exercised by the test suite. In Angular projects, Istanbul (now branded as nyc) is commonly used for this purpose, and it integrates directly into the Angular CLI test command.

Code coverage reports can be useful in two ways:

• Enforce a minimum threshold — configure your CI pipeline to fail if coverage drops below a team-agreed level, preventing untested code from merging.
• Identify under-tested areas — low-coverage files are candidates for refactoring and additional tests.

That said, coverage is just a metric. A high coverage percentage does not guarantee meaningful tests. It is entirely possible to write tests that exercise many lines of code without asserting anything useful. Coverage should inform decisions, not drive them.

Important Considerations and Limitations

TDD Does Not Replace a Full Testing Strategy

TDD focuses on unit tests — verifying a single isolated piece of logic. A comprehensive quality strategy also requires integration tests, end-to-end tests, performance tests, and acceptance testing. TDD is one important layer, not the entire testing pyramid.

Test Quality Must Not Be Sacrificed for Speed

On large projects with thousands of units of code, the temptation to skip or bypass failing tests to ship features faster is real. Ignoring failing tests corrupts the feedback loop and makes it impossible to know the actual health of the application. Discipline around test quality must be treated as non-negotiable.

Stakeholder Buy-In Is Essential

TDD requires upfront investment in writing tests before any feature code exists. Without buy-in from business stakeholders and management, this investment is often perceived as wasted time. Teams adopting TDD should invest time early in communicating the long-term quality and maintenance benefits to all parties involved.

Conclusion

Test-driven development is not a silver bullet, but it is a well-established discipline that directly improves software quality, encourages better design, and reduces the cost of change over time. Its core insight — that test code deserves the same care, time, and resources as production code — is as relevant in a modern DevOps pipeline as it was when the practice was first articulated. For teams serious about sustainable software delivery, TDD is a foundational practice worth the investment.

The Scale of the Problem

A Kenna Security study scanning the top 1,000 images on Docker Hub found that a majority contained at least one notable security vulnerability. More concerning: one in five — roughly 20% — contained what the scoring methodology classified as a critical security flaw. Several of the most widely downloaded images, with more than 100 million pulls, were found to carry hundreds of open CVEs (Common Vulnerabilities and Exposures).

CVEs are standardized identifiers maintained by MITRE for known security vulnerabilities. Each CVE is typically assigned a CVSS (Common Vulnerability Scoring System) base score on a 0–10 scale:

• 0.0–3.9: Low severity
• 4.0–6.9: Medium severity
• 7.0–10.0: High severity

Gamblin’s analysis found that many of the top containers had at least one CVE rated high severity on the CVSS scale.

Kenna Risk Scoring Added Context

In addition to CVSS scores, Gamblin applied Kenna Security’s proprietary Kenna Risk Score, which correlates additional variables including exploitability and observed attacker activity to produce what the firm describes as a more operationally accurate risk rating. The Kenna scale runs from 0 to 1,000:

• 0–329: Low risk
• 330–659: Medium risk
• 660–1,000: High risk — should be immediately remediated

Applying this scoring to the top 1,000 Docker Hub images produced striking results:

• More than 60% of images scored at or above 330 (medium risk or higher)
• More than 20% scored above 660, placing them in the high-risk category

Abandoned and End-of-Life Images Are the Worst Offenders

Gamblin noted that the images carrying the highest number of vulnerabilities tended to be the oldest ones — images that appear to be abandoned or officially end-of-life (EOL). When a maintainer stops actively updating an image, security patches stop flowing in, and the CVE debt accumulates over time.

This is a pattern familiar to anyone managing software dependencies: unmaintained packages become liabilities. The Docker image ecosystem is no different. Pulling a popular image with tens of millions of downloads does not guarantee it is actively maintained or patched.

“If an organization is using a container with a large number of open, high-risk vulnerabilities, they should be concerned with patching the vulnerabilities, building their own container, or migrating to a better-maintained one.”

— Jerry Gamblin, Principal Security Engineer, Kenna Security

These Are Code Vulnerabilities, Not Docker Vulnerabilities

Rani Osnat, VP of Product Marketing at Aqua Security, made an important distinction when responding to the findings. The vulnerabilities found in these images are not flaws in Docker itself — they are flaws in the code that Docker runs inside containers. Docker is the runtime; the vulnerable packages are the contents.

“Known vulnerabilities in container images continue to be a key concern for anyone using containers, and they should be because they are the easiest thing for attackers to exploit.”

— Rani Osnat, VP Product Marketing, Aqua Security

Osnat emphasized that scanning for these vulnerabilities is both straightforward and highly recommended at multiple stages:

• During CI/CD pipeline builds (before images are pushed)
• In container registries (catching vulnerabilities before deployment)
• At runtime, using compensating controls when patches cannot be immediately applied

Runtime Controls That Reduce Exposure

When a patch is unavailable or impractical to apply immediately, Osnat recommended several runtime security controls to limit the blast radius of a vulnerable container:

• Vulnerability shielding: Blocks known exploit patterns at the network or system-call level without requiring a patch
• Drift prevention: Prevents new binaries or code from being injected into a running container after it has been deployed
• Behavioral whitelisting: Learns the expected behavior of a container at baseline and flags or blocks anomalous activity that may indicate exploitation

A Notable Docker Runtime Vulnerability: CVE-2018-15664

The research was published in the context of an independently discovered Docker security flaw. In May 2019, Aleksa Sarai, a senior software engineer at SUSE, disclosed CVE-2018-15664, a path-traversal vulnerability in the docker cp command.

The docker cp command copies files between a container’s filesystem and the host machine. The bug allowed an attacker with access to the Docker API to read or write arbitrary files on the host — a significant privilege escalation risk. Sarai submitted a patch upstream for code review after disclosure.

“As far as I’m aware there are no meaningful protections against this kind of attack (other than not allowing ‘docker cp’ on running containers).”

— Aleksa Sarai, Senior Software Engineer, SUSE

This flaw is distinct from the image-level CVEs in Gamblin’s research but illustrates a broader point: the container security surface spans both the runtime and the image contents.

Practical Recommendations for DevOps Teams

The findings reinforce several security hygiene practices that DevOps and platform engineering teams should have in place:

1. Vet the source of every base image. Prefer Docker Official Images or Verified Publisher images on Docker Hub. Understand the maintenance cadence before adopting an image.
2. Integrate image scanning into CI/CD. Tools such as Trivy, Grype, Snyk Container, and Aqua Trivy can scan images for known CVEs before they are pushed to a registry or deployed to a cluster.
3. Scan your registry continuously. New CVEs are published after images are built. Registry-level scanning (e.g., Docker Hub’s built-in scanning, Amazon ECR scanning, or Harbor with Trivy) catches vulnerabilities that emerge post-build.
4. Pin and regularly update base images. Avoid latest tags. Pin to a specific digest, and build a process to pull updated base images on a regular cadence.
5. Avoid abandoned images. If a Docker Hub image has not been updated in over a year and has open high-severity CVEs, consider building your own or switching to a maintained alternative.
6. Apply runtime controls. Implement Pod Security Admission (Kubernetes), seccomp profiles, AppArmor/SELinux policies, and read-only root filesystems to reduce the attack surface even when vulnerable packages are present.
7. Keep the Docker runtime patched. CVE-2018-15664 and similar runtime flaws are separate from image content vulnerabilities. Maintain your container runtime versions alongside your image hygiene practices.

The Bottom Line

Popularity is not a proxy for security. An image with 100 million pulls may still carry hundreds of unpatched CVEs — particularly if it has been abandoned by its maintainer. Organizations need to treat container image provenance and freshness with the same rigor applied to any other software dependency.

“Organizations should not assume that a container is secure because it’s hosted on Docker. Ultimately, the goal of this project is to shine a light on these containers and educate users on potential risks to drive better security practices.”

— Jerry Gamblin, Kenna Security

Container security is a shared responsibility: maintainers must keep images updated, and consumers must validate what they pull, scan what they build, and monitor what they run.

Why Agile Still Matters

Agile has earned its place in the business lexicon. For almost two decades it has helped companies of all sizes break free from the inflexibility of traditional top-down hierarchies and rigid long-term planning. You will hear talk of “squads”, “chapters” and “tribes”, and although different flavours of Agile exist, certain patterns recur: teams work in short sprints, they are empowered to make decisions, and success is measured by responsiveness to customer demands rather than adherence to a fixed plan.

Justin Rodenbostel, Vice President at digital transformation agency SPR, argues there is no “after Agile”. He sees adjacent movements such as DevOps, Continuous Delivery, and Lean as direct extensions of Agile thinking, and believes organisations will continue drawing on the original Agile Manifesto to evolve how they work and respond to customers.

But Agile is also demanding. It requires intense, sustained commitment and depends heavily on organisational culture to succeed. According to the annual State of Agile survey, the top three challenges are general resistance to change, inadequate management support, and a culture at odds with Agile values. Below, practitioners share the most common pitfalls — and how to navigate them.

Pitfall 1: Short Sprints Can Obscure Long-Term Vision

One of Agile’s biggest draws is that annual planning is replaced by shorter-term milestones. Teams focus on delivering value now rather than executing a fixed multi-year roadmap. That responsiveness is powerful, but it carries a risk: teams can lose sight of where the organisation is ultimately heading.

Ian McClarty, CEO of global IT service provider PhoenixNAP, puts it plainly: “When teams are so heavily focused on the current sprint, communication about long-term vision may be put aside or ignored, leaving the team with a feeling that there is no grand plan they are supporting.”

The most straightforward remedy is deliberate, regular communication of larger business objectives — whether that is doubling headcount within two years or reaching a specific revenue milestone. McClarty also notes that Agile itself can reshape those goals by helping organisations fail faster and learn from mistakes sooner. He explains: “The ability to carve a project into many smaller pieces helps us create a sense of continued progress and motivation within teams, and also helps us create cleaner stop points — dropping features out of iterations to allow for faster time to market to see if the product will be successful before investing too much.”

Practical steps

• Share business-level OKRs or strategic goals at each sprint planning session so teams understand context.
• Run quarterly “big picture” reviews that sit above the sprint cycle and revisit organisational direction.
• Use product roadmaps — even lightweight ones — to communicate intent without locking in rigid plans.

Pitfall 2: The Agile Mindset Requires Constant Reinforcement

Christopher McFarlane, Agile Project Manager for Walmart Canada, identifies two distinct hurdles in building an Agile mindset: convincing internal team members of its value, and educating external customers and suppliers on how to engage with Agile teams.

“Encouraging team members and partners to understand the benefits of this new way of working — fast failure, constant communication, transparency — is the first hurdle,” McFarlane explains. “The second is a technical understanding of the components of an Agile methodology and educating stakeholders on how to engage with the Agile team, which is different from other areas of the business.”

Dr Matt Poepsel, Senior Vice President of Product at Predictive Index, found that a successful mindset shift required coaching individuals in the soft skills Agile demands: flexibility, collaboration, discipline, and a focus on results. He notes that any business transformation demands a simultaneous talent transformation: “We coached each individual by examining where their natural behaviours and approach were well-suited to some of these activities and where they needed to stretch to succeed.”

Chris Steel, Head of Transformation for North America at PA Consulting, emphasises the importance of early wins. “Where we have seen organisations struggle is where they approach it in a half-hearted way without the right sponsorship. The change to culture and ways of working required to achieve the best results is too significant without full engagement.”

Practical steps

• Identify and publicise early successes to build internal credibility for Agile practices.
• Invest in soft-skills coaching alongside technical Agile training.
• Secure visible sponsorship from senior leadership — Agile transformations stall without it.
• Create onboarding materials specifically for external stakeholders explaining how to collaborate with Agile teams.

Pitfall 3: Teams Slide Back into Traditional Management Styles

Adopting Agile is not a one-time event. Without sustained attention, teams can revert to the top-down, hierarchical structures they were accustomed to — particularly under delivery pressure.

David Fort, Managing Director at Haines Watts Manchester, describes it this way: “Being an Agile business isn’t a start-stop scenario; it’s a constant shift in culture and balance that has to be regularly revisited. If you stop running as an Agile business, you’re likely to seize up. The real challenge is ensuring the agility is fresh and team members remain focused on being Agile.”

Fort also advises factoring Agile compatibility into hiring decisions — prioritising candidates who demonstrate adaptability and comfort with ambiguity alongside technical skills and academic credentials.

Practical steps

• Schedule regular retrospectives that explicitly examine whether Agile principles are still being followed — not just whether delivery targets are being met.
• Include Agile values and behaviours in performance reviews and hiring criteria.
• Rotate Agile coaching and facilitation responsibilities to avoid over-dependence on a single champion.

Pitfall 4: Agile Does Not Scale Automatically

Rodenbostel identifies what he considers the most common mistake: assuming that success achieved by a small Agile team can be replicated at scale simply by applying the same practices to larger groups. “What works for small groups often doesn’t work for larger or different groups,” he warns. “Customers, business stakeholders, software assets, and government or industry regulation can all have an impact on what works and what does not. We see a tendency when scaling to standardise in a way that is often too prescribed.”

He also notes that the Agile move away from big upfront annual planning is particularly challenging in publicly traded companies, where investor expectations can push leadership back toward rigid, predictable roadmaps. The goal, he says, is to determine the appropriate balance rather than insisting on Agile purity in every context.

Practical steps

• Treat scaling frameworks (SAFe, LeSS, Nexus, etc.) as starting points, not prescriptions — adapt them to your specific constraints.
• Run a deliberate pilot with a small team before scaling Agile practices organisation-wide, and document what worked and what did not.
• Acknowledge regulatory and investor-relations constraints explicitly and design your Agile practice around them rather than ignoring them.
• Avoid standardising processes across teams prematurely — let teams develop practices that suit their context before rolling out a uniform model.

The Bottom Line

Agile is not a silver bullet, but it is a durable and well-evidenced approach to building adaptive, customer-focused teams. Its failures are rarely about methodology — they are almost always about culture, commitment, and communication. The organisations that get the most out of Agile are those that treat it as an ongoing practice rather than a project to be completed, invest in the human side of the transformation, and are honest about the contexts where a more structured approach remains necessary.

As Rodenbostel puts it, the concepts in the original Agile Manifesto are not going away. The challenge is applying them with the discipline and self-awareness they demand.

What Is Continuous Integration Software?

Continuous Integration (CI) software automates the process of merging developer code changes into a shared repository frequently — typically multiple times per day. Each merge triggers an automated build and test sequence, catching integration errors early and reducing the time and cost of software defect remediation. CI is a foundational practice in modern DevOps pipelines and is tightly coupled with Continuous Delivery (CD) workflows.

Adoption of CI tooling has accelerated as organisations of all sizes shift toward agile development, microservices architectures, and cloud-native deployments. This has created a competitive and fragmented vendor landscape spanning open-source projects, commercial SaaS platforms, and cloud-provider-native services.

Major Players in the CI Software Market

The following tools and vendors were identified as key participants in the global CI software market as of 2019:

• Jenkins — The dominant open-source CI server with a large plugin ecosystem, widely self-hosted.
• CircleCI — A cloud-native CI/CD platform popular with startups and mid-market engineering teams.
• Travis CI — An early SaaS CI platform with strong adoption in open-source communities.
• TeamCity (JetBrains) — A commercially supported CI server with deep IDE integration.
• Bamboo (Atlassian) — Tightly integrated with Jira and Bitbucket for enterprise teams.
• GoCD (ThoughtWorks) — An open-source tool focused on pipeline visualisation and modelling.
• AppVeyor — A Windows-first CI service popular in the .NET ecosystem.
• Codeship — A hosted CI service targeting developer simplicity and fast onboarding.
• Drone.io — A container-native CI platform built around Docker.
• Semaphore — A fast, hosted CI/CD service with a focus on build speed.
• Bitrise — A mobile-first CI/CD platform targeting iOS and Android development.
• Buildbot — A Python-based, highly configurable open-source CI framework.
• CruiseControl — One of the earliest CI servers, largely superseded by newer tools.
• Hudson — The project from which Jenkins was forked; still maintained by the Eclipse Foundation.
• Puppet Pipelines (formerly Distelli) — A deployment-focused pipeline tool in the Puppet ecosystem.
• AWS (CodeBuild, CodePipeline) — Cloud-provider-native CI/CD services integrated with the AWS ecosystem.
• Microsoft (Azure DevOps / Azure Pipelines) — Enterprise CI/CD tightly integrated with Azure and GitHub.
• Chef — Infrastructure automation tooling often used alongside CI pipelines for configuration management.

Market Segmentation

By Deployment Type

• Cloud-Based — Managed SaaS CI platforms where the vendor operates the infrastructure. Examples include CircleCI, Travis CI, Semaphore, and AWS CodeBuild. Preferred for lower operational overhead and elastic scaling.
• Web-Based (Self-Hosted) — Tools deployed on-premises or on private cloud infrastructure. Jenkins, GoCD, and Buildbot fall into this category. Preferred by organisations with strict data residency, compliance, or customisation requirements.

By End-User Organisation Size

• Large Enterprises — Typically require on-premises or hybrid deployment options, role-based access control, audit logging, SSO integration, and SLA-backed support. Vendors such as Microsoft (Azure DevOps), JetBrains (TeamCity), and Atlassian (Bamboo) target this segment.
• SMEs (Small and Medium-Sized Enterprises) — Often favour managed SaaS platforms that reduce infrastructure management burden, with transparent per-seat or per-build-minute pricing. CircleCI, Semaphore, and Codeship are common choices.

Regional Market Coverage

The market research covered CI software adoption, production value, and consumption across the following regions, with historical data from 2014–2019 and forecasts through 2026:

• North America — The United States represents the largest single market, home to many leading CI vendors and a high density of agile-native software organisations.
• Europe — Including Germany, UK, France, Italy, Spain, Russia, and Poland. Strong enterprise DevOps adoption, particularly in financial services and manufacturing sectors.
• China — Rapid growth driven by domestic technology giants and government digital transformation initiatives.
• Japan — Mature software market with growing DevOps adoption among large IT service providers.
• India — A significant and fast-growing market, driven by a large software services industry and startup ecosystem.
• Southeast Asia — Including Malaysia, Singapore, Philippines, Indonesia, Thailand, and Vietnam.
• Central and South America — Including Brazil, Mexico, and Colombia.
• Middle East and Africa — Including Saudi Arabia, UAE, Turkey, Egypt, South Africa, and Nigeria.
• Other Regions

Market Dynamics

Growth Drivers

• Accelerating adoption of agile and DevOps methodologies across industries beyond software-native companies.
• Shift to cloud infrastructure reducing the barrier to deploying or consuming CI tooling.
• Growth of microservices and containerised application architectures, which require automated integration pipelines to manage complexity.
• Increasing developer headcount globally, expanding the addressable market for per-seat and usage-based CI pricing models.
• Regulatory and competitive pressure on software delivery speed in sectors such as financial services, healthcare, and retail.

Limitations and Challenges

• High fragmentation in the vendor landscape makes vendor selection complex for buyers.
• Self-hosted open-source tools (notably Jenkins) carry hidden operational costs in maintenance, plugin management, and security patching.
• Organisations in regulated industries face data residency and compliance constraints that limit SaaS CI adoption.
• Cultural resistance to DevOps transformation remains a barrier in large, traditional enterprises.

Opportunities

• Emerging markets in Southeast Asia, India, and Africa represent significant greenfield opportunities as developer communities grow.
• AI-assisted CI features — such as intelligent test selection, flaky test detection, and build failure prediction — are an emerging differentiator.
• Mobile CI (Bitrise, Fastlane integrations) remains an under-penetrated segment relative to server-side CI maturity.
• Government and public sector digital transformation programmes globally are creating new enterprise CI demand.

Report Structure Overview

The source market research report was structured across the following analytical sections:

1. CI Software introduction, market scope, and size estimation (including market concentration ratio and maturity analysis)
2. Global value and growth rate: 2014–2026
3. Market segmentation by type and application
4. Regional production value and growth rates: 2014–2019 (historical) and 2019–2026 (forecast)
5. Competitive landscape: market share and company profiles of key participants
6. Product specification, technology, and production analysis (revenue, cost, gross margin)
7. Regional market value and consumption forecasts: 2019–2026
8. New project feasibility analysis, including SWOT analysis for new market entrants and investment suggestions

Takeaways for DevOps Practitioners

While market research reports like this are primarily targeted at investors and business strategists, they contain useful signals for DevOps engineers and engineering leaders:

• Vendor consolidation is ongoing. Several tools listed (Hudson, CruiseControl, Codeship) have seen declining adoption or acquisition since this report was produced. Evaluating long-term vendor viability matters when selecting CI tooling.
• Cloud-native CI is winning on convenience. The shift from self-hosted to SaaS CI reflects broader infrastructure trends. Unless there are strong compliance reasons to self-host, managed CI reduces operational burden significantly.
• Tool proliferation creates integration complexity. As the CI vendor landscape grows, so does the need for standardised pipeline-as-code approaches (Jenkinsfile, .circleci/config.yml, .github/workflows) that reduce vendor lock-in.
• Regional market growth matters for platform choice. Teams operating across multiple geographies should evaluate whether their CI vendor offers data residency options and low-latency build runners in their key regions.

Source: portbulletin.com — Global Continuous Integration Software Market Report, 2019.

Overview

As DevOps adoption accelerated across enterprises in 2019, the global market for DevOps tooling attracted significant analyst attention. A research report published in May 2019 — covering the period from 2014 through a 2026 forecast horizon — profiled the competitive landscape, segmented demand by tool maturity and industry vertical, and mapped regional growth trends across North America, Europe, and Asia-Pacific.

The report used a SWOT framework (Strengths, Weaknesses, Opportunities, Threats) to evaluate both the overall market and individual vendor positions. It examined organizational objectives including product portfolio depth, production scale, and financial health of key participants.

Market Maturity Segmentation

One of the more useful structural elements of the report is its three-tier classification of DevOps tooling by organizational maturity level:

• DevOps Ready: Organizations or toolsets that have established the foundational prerequisites for DevOps — typically including version control, basic CI pipelines, and cross-functional team structures.
• DevOps Enabled: Environments where automation, continuous integration, and continuous delivery pipelines are operational and integrated across development and operations workflows.
• DevOps Capable: Mature implementations featuring full pipeline automation, infrastructure-as-code, real-time monitoring, feedback loops, and measurable deployment frequency improvements.

This maturity ladder is a useful planning framework for engineering teams assessing where their toolchain investments should focus next.

Key Vendors Covered

The report profiled eighteen vendors, covering company background, product positioning, and revenue contribution to the DevOps tools segment from 2014 to 2019. The vendors included:

• Puppet Labs — configuration management and infrastructure automation
• Chef — infrastructure-as-code and compliance automation
• Docker Inc. — containerization platform
• Red Hat (Ansible) — agentless IT automation and orchestration
• Atlassian — collaboration, issue tracking (Jira), and CI/CD (Bamboo, Bitbucket Pipelines)
• SaltStack — event-driven infrastructure automation
• CA Technologies — enterprise DevOps and release automation
• Rackspace — managed cloud and DevOps professional services
• XebiaLabs — release orchestration and deployment automation
• VersionOne — agile project management and DevOps planning
• Cisco — networking and infrastructure for DevOps environments
• CollabNet — agile lifecycle management and version control
• HP (Micro Focus) — application delivery and testing automation
• IBM — hybrid cloud DevOps and UrbanCode tools
• Microsoft — Azure DevOps and GitHub ecosystem
• Spirent Communications — network and application test automation
• VMware — virtualization and cloud-native infrastructure
• DBmaestro — database DevOps and release automation

Industry Verticals Driving Demand

DevOps tooling demand in 2019 was not uniform across sectors. The report segmented application demand across six industry categories:

• IT & Technology: The largest and earliest adopter segment, driven by software product companies and cloud service providers.
• Banking, Financial Services & Insurance (BFSI): Regulatory complexity and legacy system modernization made DevOps tooling critical for controlled, auditable release pipelines.
• Retail: E-commerce growth and the need for rapid feature deployment during peak periods accelerated DevOps adoption.
• Telecom: Network function virtualization and the shift to software-defined networking created demand for infrastructure automation tools.
• Education: Digital learning platforms and SIS modernization projects introduced DevOps practices into higher education IT.
• Others: Healthcare, government, manufacturing, and other sectors with emerging DevOps programs.

Regional Coverage

The report analyzed DevOps tools market size and growth rates across the following regions for the 2014–2026 period:

• United States
• Europe
• China
• Japan
• Southeast Asia
• India
• Central & South America

North America — led by the United States — was positioned as the dominant market, with Europe and China identified as significant secondary growth regions. India and Southeast Asia were highlighted as fast-growing markets driven by IT services outsourcing and domestic digital transformation initiatives.

Report Structure and Study Objectives

The full report was structured across twelve major sections. Key analytical objectives included:

• Analyzing global DevOps tools market status and future forecast through 2026
• Identifying growth opportunities by region, vertical, and tool category
• Profiling key vendors and analyzing their competitive strategies and development roadmaps
• Defining and forecasting the market by product maturity type and application vertical
• Mapping market share distribution by region for 2014–2019 and projecting through 2026

Stakeholders identified as primary consumers of this research included DevOps tool manufacturers, solution distributors, subcomponent and integration vendors, industry associations, and downstream enterprise IT buyers.

Context for DevOps Engineers

While market research reports of this type are primarily aimed at investors and business strategists, the vendor landscape and segmentation data are directly relevant to DevOps practitioners making toolchain decisions. The vendor list reflects the competitive set that dominated enterprise procurement conversations in 2019, spanning configuration management (Puppet, Chef, Ansible, SaltStack), containerization (Docker), CI/CD orchestration (XebiaLabs, Atlassian), and platform-level offerings from IBM, Microsoft, and VMware.

The three-tier maturity model — Ready, Enabled, Capable — also maps reasonably well to practical DevOps assessment frameworks used by engineering teams to benchmark their own pipeline maturity and prioritize investments in automation, observability, and feedback loop optimization.

Source: industrydailyobserver.com, May 2019. Original report: “DevOps Tools Market – Market Demand, Growth, Opportunities, Analysis of Top Key Players and Forecast to 2026.”

As companies try to keep up with the pace of customer demands in software development and deployment, successful companies are adopting DevOps strategies. This brings together software developers with IT tech operations teams to work as a single unit to speed up the time to market.

In today’s hyper-competitive market, getting quality code out fast is key. Equipping DevOps teams with the right software and automation provide a variety of advantages:
1. Increased team flexibility (and agility)
2. Cross-skill and cross-functional development
3. Better collaboration
4. Improved support
5. Faster bug detection and remediation
6. More efficient processes
7. Automation of many manual tasks to help avoid manual errors
8. More engaged team members

Teams are using a variety of DevOps tools that integrate with each other as they create agile software environments.

Popular DevOps Tools

Here are some of the most popular DevOps tools chosen by successful teams.

1. Git

Git is in use across the software development industry. A distributed source code management tool, it allows you to track development progress. It creates milestones that allow you to save different versions of source code. When needed, you can go back to previous builds. It allows for the creation of separate “branches” which fosters experimentation and no-risk innovation.

2. JFrog Artifactory

JFrog Artifact Repository is one of the most popular choices among high-performing DevOps teams. In fact, it is used by 70% of Fortune 100 teams. As an artifact repository manager, it is able to integrate with any tech and supports software in different languages, including DevOps tools such as the Docker Registry. It supports total end-to-end management. It is the choice for companies looking to accelerate their continuous product delivery cycle. It also tracks artifacts from development through production to deployment.

3. Docker

Docker is one of the top container platforms. It isolates applications and chunks into different containers. This makes them portable and secure. When coupled with automation services, it can significantly improve workflow delivery while allowing containers to be moved from one location to different ones. Docker integrates with both Jenkins and Bamboo. For cloud migrations, major providers such as Google Cloud and AWS feature Docker support.

4. Jenkins

Jenkins can run right out of the box on Windows, Mac OS X, and Linux. It features an easy install and an intuitive interface in an open source solution. Jenkins allows you to automate different phases of the delivery pipeline and automate many manual tasks to free up your team for bigger projects. There are more than 1,000 plugins that can integrate with other DevOps tools, such as Docker or Puppet. It allows you to customize your CI/CD pipeline to your specifications.

5. Bamboo

Bamboo can automate your delivery pipeline by taking projects from builds to deployment. Bamboo boasts more built-in options, which means fewer (if any) plugins are needed than found in open source solutions. Many functions that need to be set up manually in products like Jenkins are pre-built in Bamboo. It works with other products from the Atlassian company line, including Bitbucket and Jira.

6. Puppet

An open source software solution, Puppet’s config management, and deployment tool are used to manage multiple application services. A unified platform allows for customization of configurations to aid with automated remediation. It can work with applications, network devices, and cloud-based servers. Puppet also integrates with many popular DevOps tools and feature more than 5,000 modules.

7. Vagrant

Vagrant allows the building and managing of virtual machine environments rolled into a single workflow. It can help you reduce your environment setup time. Vagrant creates single files for projects and focuses on automation. Whether you are a developer, operator, designer, or part of an integrated DevOps team, it can describe all of your software requirements, packages, OS configs, and users.

Which DevOps Tools Are Right For You?

Those listed above are just a few of the most popular choices. Deciding which tools are right for you will take some testing. Open source tools will save you money, but they will take more time to set up and configure. Most of the commercial tools have free-to-try versions to allow for evaluation.
In the end, it boils down to three key things:
1. The functionality you need
2. The budget you have (both time and money
3. How your team works best

What Electric Cloud Released

Electric Cloud announced the Winter 2019 edition of its ElectricFlow platform in February 2019, targeting a long-standing problem in enterprise DevOps adoption: the tools are powerful, but they are often too complex for anyone outside a core engineering team to use effectively. The update focused on usability improvements that lower the barrier to entry without reducing the platform’s underlying capability.

Key Features in the Winter 2019 Release

Role-Based Personas and Tailored UIs

The most notable change in this release was the introduction of role-based personas. Rather than presenting every user with the same dense, feature-rich interface, ElectricFlow now allows users to select a persona that matches their organizational role. Each persona surfaces only the information and controls most relevant to that role, reducing cognitive overload for non-specialist users.

According to Sam Fell, Vice President of Marketing at Electric Cloud, the intent is to make the platform usable by a wider population—not just senior engineers who already understand the underlying DevOps concepts, but also project managers, QA leads, and release coordinators who need visibility without needing to master the full toolset.

Task-Oriented Login Experience and Bookmarks

The Winter 2019 release also introduced a customizable login experience oriented around frequent tasks rather than a generic dashboard. Users can now save bookmarks or favorites to jump directly to the pages and pipelines they access most often, reducing the number of clicks required to reach common workflows.

This kind of quality-of-life improvement is often underestimated in enterprise tooling, but the friction caused by slow navigation compounds quickly across large teams working under delivery pressure.

Release and Pipeline Templates with Git Triggers

Electric Cloud also added release and pipeline templates designed to accelerate onboarding. Templates can be configured to respond to user-defined events and triggers—including code check-ins to a Git repository—so teams can get pipelines running quickly without building everything from scratch.

For organizations trying to standardize DevOps practices across multiple teams, reusable templates represent a practical forcing function: they encode institutional best practices and reduce the variability that comes when each team designs its own pipeline from the ground up.

The Broader Problem: DevOps Adoption Is Still Uneven

Electric Cloud’s usability push addresses a genuine and widely recognized challenge: most organizations have not achieved organization-wide DevOps adoption. What typically exists instead is a pattern of pockets of DevOps—small, highly motivated cross-functional teams continuously delivering a specific set of applications, while the rest of the organization continues to rely on older, more familiar tools and processes.

This is not simply a cultural problem, though culture is often blamed. A significant contributing factor is the skill gap: the engineers who pioneered DevOps within an organization typically have a higher baseline of automation and infrastructure knowledge than the broader IT population. When organizations try to scale DevOps beyond the initial team, the tooling that worked for experts can intimidate or block less specialized colleagues.

What looks like organizational resistance or cultural inertia is often something more concrete: an IT professional who is already proficient with an older tool will frequently reach for that tool rather than invest time in mastering a more capable but less familiar platform—especially when delivery deadlines are pressing.

Automation and Human Oversight: A Deliberate Balance

Fell noted that while modern DevOps platforms—including ElectricFlow—increasingly incorporate machine learning and automation to reduce manual toil, this creates its own accessibility problem. Highly automated systems can become black boxes: they produce results that users cannot easily interpret, troubleshoot, or override.

Electric Cloud’s stated strategy is to layer automation on top of a transparent, human-readable interface rather than replacing human judgment with opaque automation. The persona-based UI is part of this approach: it gives each user a clear view of what is happening at a level of abstraction appropriate to their role, without drowning them in raw pipeline data.

What This Means for DevOps Teams in Practice

The features in ElectricFlow’s Winter 2019 release reflect a broader design philosophy that is worth taking seriously regardless of which platform your organization uses:

• Role-appropriate interfaces reduce errors. When users see only the controls relevant to their role, they are less likely to make changes outside their area of responsibility or to be overwhelmed by irrelevant information.
• Templates accelerate standardization. Providing pre-built, Git-integrated pipeline templates lowers the effort required to onboard new teams and reduces pipeline-to-pipeline variation.
• Navigation friction is a real cost. Bookmarks and task-oriented dashboards are small improvements with a measurable impact on daily workflow efficiency across large teams.
• Accessibility is a prerequisite for scale. No DevOps platform delivers organizational value if only a subset of the team can use it confidently. Tooling that meets users where they are—rather than requiring everyone to level up to the tool—scales more reliably.

Conclusion

Electric Cloud’s Winter 2019 ElectricFlow release is a practical example of a DevOps vendor acknowledging that feature richness and usability are not the same thing. By investing in persona-based interfaces, streamlined navigation, and opinionated pipeline templates, the company signaled a shift toward meeting enterprise teams at their actual skill level rather than their aspirational one.

More accessible tooling alone will not complete a DevOps transformation—process changes, organizational buy-in, and skill development are all required. But reducing the interface friction that slows adoption is a necessary step, and it is one that every DevOps platform vendor should be actively investing in.

What Is Jenkins Configuration as Code?

Jenkins has long been the de facto CI/CD system for enterprises, but managing it has traditionally required a dedicated administrator who manually configures Jenkins and its large ecosystem of plugins through verbose XML files. CloudBees addressed this operational burden by releasing version 1.0 of the Jenkins Configuration as Code (JCasC) plugin, which allows administrators and developers to define Jenkins configurations in human-readable YAML instead of XML.

With JCasC, teams can:

• Write Jenkins configuration in plain-text YAML files that are easy to read and edit
• Store those configuration files in a version control system such as Git
• Validate and test configuration changes before applying them
• Reuse a single configuration file to set up multiple Jenkins instances consistently

Why Configuration as Code Matters for Jenkins

Jenkins supports over 1,400 plugins, and configuring each one manually through the Jenkins UI or raw XML is error-prone, time-consuming, and difficult to audit. JCasC applies the same principles used in modern software development — version control, code review, repeatability — to Jenkins configuration management.

Mike Wolf, managing director and modern delivery leader specializing in DevOps and Agile at KPMG, speaking at DevOps World | Jenkins World 2018 in San Francisco, noted that configuration as code removes tedious manual processes and supports more industrialized, scalable use of Jenkins across large organizations. Equally important, it encourages system administrators to adopt the rigor and discipline of the modern software development lifecycle.

Mik Kersten, CEO at Tasktop Technologies, framed it as part of a broader industry shift:

“Over the past decade, the DevOps community has started taking all of these deployment and configuration steps and thinking of them as a part of the program — as code. This means we can start applying programming concepts, such as immutability, to the entire delivery pipeline.”

— Mik Kersten, CEO, Tasktop Technologies

Is YAML Good Enough for Jenkins Configuration?

Not everyone in the DevOps community is convinced that YAML is the right medium for configuration as code. Companies such as Atomist and Pulumi have pushed further, enabling developers to express configuration and infrastructure using real programming languages — JavaScript, TypeScript, and Python — rather than markup or domain-specific languages.

Rod Johnson, founder and CEO of Atomist, argued that YAML has inherent limitations when describing complex behaviors:

“There is a role for configuration, but whenever you are describing rich behaviors, code is a lot better. Too often configuration like YAML becomes a bad imperative programming language.”

— Rod Johnson, CEO, Atomist

Kohsuke Kawaguchi, then CTO of CloudBees and creator of Jenkins, acknowledged these alternative approaches but defended JCasC’s pragmatic scope: the plugin is designed to be “good enough for ordinary users of Jenkins.” His reasoning is that user adoption itself drives the plugin’s evolution — as more teams use JCasC, they will push its boundaries and drive further development.

The Broader Shift: Infrastructure and Configuration as Real Code

JCasC sits within a broader industry movement toward treating all infrastructure and configuration decisions as software artifacts. Joe Duffy, CEO of Pulumi, described this shift:

“Configuration as code has been a key addition in the DevOps toolkit over the past decade, to apply more rigorous software engineering practices to configuration problems across an enterprise infrastructure, from virtual machines and containers to databases and networks.”

— Joe Duffy, CEO, Pulumi

Key engineering practices that become possible when configuration is treated as code include:

• Version control and change history: Every configuration change is tracked with a commit message and author attribution
• Code review: Peer review of configuration changes catches errors before they reach production
• Automated CI/CD pipelines: Configuration files can be tested and deployed through the same pipelines used for application code
• Reusable abstractions: Shared modules and packages allow teams and communities to standardize common configurations
• Unit testing: When configuration is expressed in a real programming language, it can be unit tested — something that is difficult or impossible with YAML or XML alone

Atomist’s Johnson also pointed out that code written in modern languages benefits from IDE tooling — syntax highlighting, autocomplete, refactoring support, and static analysis — advantages that flat configuration files cannot offer. Modern languages are also inherently more modular and reusable, making it easier to share and maintain configurations at scale.

Practical Takeaways for DevOps Teams

Whether a team adopts JCasC’s YAML-based approach or a full code-first tool like Pulumi, the underlying principle is the same: Jenkins and infrastructure configuration should be managed with the same discipline as application code. For most Jenkins shops, JCasC offers a practical, low-friction entry point:

• Start by exporting your existing Jenkins configuration to a YAML file using the JCasC plugin
• Commit that file to a Git repository and establish a pull-request review workflow for future changes
• Use the same YAML file to spin up staging or test Jenkins instances for validation before applying changes to production
• As complexity grows, evaluate whether a full programming-language approach (Pulumi, Atomist, or Groovy-based Jenkinsfile pipelines) better fits your team’s needs

The DevOps community’s direction is clear: configuration is too important to manage manually. Tools like JCasC lower the barrier to adopting this discipline without requiring teams to overhaul their existing Jenkins investments.