A global survey of 1,350 business and IT executives conducted by Jigsaw Research on behalf of NTT Ltd., a provider of IT services, suggests organizations are a lot more agile in the wake of the COVID-19 pandemic, but there’s still a significant gap in terms of prioritizing agility.

A full 89% of business and IT leaders agree COVID-19 has caused significant changes to their operating processes, with 87% also agreeing it has accelerated their digital transformation strategy. A third of respondents (33%) also noted they pivoted their technology focus to take advantage of a market opportunity or scale their operations.

However, well over a third of operational executives identified speed and agility as a core component of technology strategy, while only just over half of IT executives agreed (53%).

Overall, the survey found there is a consensus IT remains a key enabler of business efficiency. A total of 91.4% respondents agreed that enabling business efficiency is a key driver for their technology strategy, with 92% of IT respondents saying their overall technology strategy is aligned either fully or partially with the organization’s business strategy needs.

However, there’s a significant divide in terms of the importance of IT’s role in driving business efficiency. A technology strategy that drives business efficiency opportunities is seen as being most crucial by 70% of operations teams, yet only 48% of IT teams said the same.

Sanjay Lakhani, a principal consultant for NTT and head of the IT service management (ITSM) practice for the Americas region said that while the alignment between business operations and IT is not as close as it could be, a lot of improvement has been made. Prior to the COVID-19 pandemic, the divide between IT and the rest of the business was generally wider, noted Lakhani.

Now that more organizations are realizing how dependent they are on software, there’s a lot more focus on application development with business units, he added. There’s also more pressure on IT operations teams, which are being asked to keep pace with the rate at which digital business transformation initiatives have accelerated, added Lakhani.

Unfortunately, the survey also highlighted an emerging technology gap. The survey found 92% of business and IT leaders agreed emerging technology is crucial for their technology strategy, but only two-fifths (41%) believed they have the technology to meet their organization’s immediate objectives. Those result suggest a significant transition is underway, both in terms of how software is constructed and the type of IT infrastructure required.

In the meantime, it remains to be seen to what degree the COVID-19 pandemic has fundamentally altered the DNA of IT operations teams, but it’s apparent enterprise IT will never be the same. Most IT teams have embraced, to varying degrees, agile application development methodologies along with DevOps best practices. The challenge now is aligning the operational cadence IT teams follow with that of the rest of the business in the wake of the pandemic.

Cloud computing skills are in high demand among organizations that are looking to operate and maintain thei organization’s cloud solutions. The pandemic and subsequent ‘new normal’ of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the cloud computing skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Pluralsight cybersecurity courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!

With this in mind, the editors at Solutions Review have compiled this list of top-rated Pluralsight cloud computing courses to consider taking. This platform is perfect for those looking to take multiple courses or acquire skills in multiple different areas, or for those who want the most in-depth experience possible through access to entire course libraries or learning paths. In sum, Pluralsight offers more than 7,000 expert-led video courses.

Note: Our editors assembled this directory by listing top-rated Pluralsight cloud computing courses in a number of different cloud coverage areas, including Amazon Web Services, Microsoft Azure, Google Cloud Platform, cloud migration and transformation, cloud security, and cloud management.

Architecting for Security on AWS
“AWS security architects need to understand how to build security into every AWS deployment at every level. In this course, Architecting for Security on AWS, you’ll learn how to secure your data and your AWS services and resources at multiple levels using a defense-in-depth approach. First, you’ll learn how to protect your AWS credentials using identity and access management. Next, you’ll see how to capture and analyze logs using CloudTrail, CloudWatch, and Athena. Finally, you’ll learn how to implement network and instance security, encrypt data at rest and in-transit, and set up data backup, replication, and recovery. After finishing this course, you’ll be ready to granularly control access to your AWS resources.”

Cloud Computing: The Big Picture
Description: “You hear a lot about cloud computing these days. Ever wonder what that means, and where it fits into your daily job? That’s what this course is designed to teach you. You’ll explore applications in the cloud (commonly called Software as a Service, or SaaS), an overview of cloud platforms, including Amazon Web Services and Microsoft Azure, as well as private clouds (bringing cloud technology on-premise). By the time you’re done, you’ll know what cloud computing is all about and be ready to start exploring specific implementations.”

Cloud-Native Architecture: The Big Picture
Description: “You’re asked to build more changeable, available, scalable, and secure software. While cloud platforms make some of those characteristics easier to achieve, it’s more about how you build software, not where you run it. In this course, Cloud-native Architecture: The Big Picture, you’ll learn what it means to be cloud-native.”

Cloud Security: Introduction to Certified Cloud Security Professional (CCSP®)
“Many organizations may make the mistake of skipping architecture and going straight to design. This can be avoided by selecting a cloud service provider that fits with your business requirements. In this course, Cloud Security: Introduction to Certified Cloud Security Professional (CCSP), you will learn foundational knowledge of how to translate business requirements into selection criteria for a cloud service provider. First, you will learn how to identify which cloud architecture best meets your business requirements. Next, you will discover how to use security architecture to develop a secure cloud consumption strategy. Finally, you will explore how to select a cloud service provider and meet regulatory requirements. When you’re finished with this course, you will have the skills and knowledge needed to select and consume cloud services securely.”

Foundations for Cloud Architecture
Description: “First, you’ll explore the differences between public, private, and hybrid cloud computing, the business requirements they fill, and their architectural caveats. Next, you’ll learn about the practical variations in public cloud service offerings. Finally, you’ll take a look into how you and your business can benefit from advanced cloud services.”

Fundamental Cloud Concepts for AWS
“AWS certifications add value for both technical and non-technical resources in today’s digital world. In this course, Fundamental Cloud Concepts for AWS, you will walk through the fundamental cloud concepts which explains one of the four major areas that are covered by the AWS Certified Cloud Practitioner Exam. First, you will review the concept of cloud computing and the value it provides to organizations. Next, you will learn the organization of AWS global infrastructure. Next, you will discover the economics of cloud computing. Finally, you will review the tools and services that AWS provides to support infrastructure running on their platform. When you are finished with this course, you will have a clear understanding of the cloud and the fundamentals needed for a portion of the AWS Certified Cloud Practitioner exam.”

Google Cloud Functions Fundamentals
“Developers want to build serverless microservices so they can create new content, reduce maintenance, scale easily, and deliver new features to users faster. In this course, Google Cloud Functions Fundamentals, you will expand your serverless skills to create high-quality Microservices that will enhance the experience of your app or website. First, you will learn to build unique functions that interact with other Google Cloud services such as Cloud Vision and Cloud Datastore. Second, you will discover advanced concepts such as the function’s file system, idempotent function design, and working through memory and timeout issues. Finally, you will explore how to properly log errors and set up monitoring for your functions so you can continue to monitor them once they are in production. When you’re finished with this course, you will have a high-level understanding of Google Cloud Functions that will allow you to create unique experiences for your customers.”

Google Cloud Platform Fundamentals
“Over the last few years, the cloud has proven itself as a successful enabler for organizations of all sizes to improve agility, scale, reliability, and spend management. It has also made available capabilities that have greatly accelerated the adoption of a wide variety of new types of applications – from big data to Internet of Things. This rapidly expanding set of capabilities can be difficult for anyone to keep up with, and that challenge is further compounded when attempting to map capabilities across different public cloud providers. This course, Google Cloud Platform Fundamentals, provides you with an extensive overview of Google Cloud Platform. While the Google Cloud Platform is a more recent offering than some of its competitors, it draws on years of experience running Google’s massive internal infrastructure and exposes a streamlined set of solution-focused capabilities to help you build great systems.”

Microsoft Azure Cloud Concepts
“Over 90% of the Fortune 500 companies use at least one Microsoft cloud service. In this day and age, it’s important for any IT or Business Professional to know what Microsoft offers from a cloud computing perspective. In this course, Microsoft Azure Cloud Concepts, you will focus on the basics of cloud computing by defining at a more general level what the cloud is, why it’s useful, and examine how Microsoft fits in each type of cloud offering. First, you will learn about what the cloud is and why it is important. Next, you will discover the different types of cloud computing. Finally, you will explore the different cloud computing deployment models. When you are finished with this course, you will gain an understanding of why the cloud is so important to today’s businesses and know the differences between, public, private, and community clouds.”

Microsoft Azure: The Big Picture
“Microsoft Azure is a huge collection of services for building applications in the cloud and can often be overwhelming. In this course, Microsoft Azure: The Big Picture, you will learn foundational knowledge to begin planning solutions using Microsoft Azure. First, you will learn about cloud computing and the different ways to run your application code. Next, you will discover the data storage, processing, and analysis capabilities in Auzre. Finally, you will explore how to create networks; integrate, manage, and secure your applications; and develop for Azure. When you’re finished with this course, you will have the skills and knowledge of Microsoft Azure needed to begin working on your cloud solutions.”

Patterns of Cloud Integration
Description: “This course looks at how developers and architectures can use remote procedures calls, asynchronous messaging, shared databases, and file transfers in cloud scenarios. We discuss pitfalls and benefits of each pattern and how the cloud introduces new design aspects. We look at a wide range of technologies from AWS, Microsoft, Salesforce.com, Mulesoft, Informatica, and more.”

CircleCI today previewed the ability to deploy applications on platforms based on Arm processors in a forthcoming release of its namesake continuous integration/continuous delivery (CI/CD) platform.

Tom Trahan, vice president of business development for CircleCI, said that Arm processors are rapidly being embraced by DevOps teams seeking to take advantage of the price/performance advantages they currently have over conventional x86 processors. Amazon Web Services (AWS), for example, makes available an AWS Graviton2 service based on 64-bit Arm Neoverse processors that it claims provides a 40% advantage in overall price/performance.

The Arm community, meanwhile, has adopted many of the libraries employed on x86 platforms, Trahan noted, which is making it simpler to automate the deployment of applications on Arm-based platforms using a CI/CD platform.

It’s not clear how quickly developers are building applications for Arm processors. Historically, Arm processors can be found in a wide range of embedded systems typically deployed at the network edge. However, cloud services based on Arm processors are also becoming more widely available, and it’s only a matter of time before more Arm-based platforms are deployed in on-premises IT environments.

Arm itself is also in the process of being acquired by Nvidia, which envisions running machine learning algorithms, and other classed of advanced workloads, on both Arm processors and graphics processing units (GPUs). Many of the early efforts to build processors optimized for running AI workloads at the network edge are based on Arm processors.

The addition of Arm platforms to any IT environment will naturally create some management challenges for DevOps teams. In some cases, developers may even decide to build applications using, for example, the latest generation of machines from Apple, that are based on Arm processors, to streamline the application development and deployment process. In other cases, however, applications that are intended to be deployed on Arm processors might still be built on x86 systems.

Regardless of approach, the arrival of more Arm-based platforms in the enterprise doesn’t mean other classes of processors are going to fade away. Rather, from a processor perspective, IT environments are about to become a lot more diverse as more workloads are deployed on a wider range of processors. It may be quite a while before Arm-based platforms usurp x86-based systems that still dominate the IT landscape. However, during any turbulent economic time, the number of organizations inclined to reduce the total cost of IT by embracing new platforms always increases.

Fortunately, as more IT infrastructure is managed as code, the easier it becomes to switch platforms. There may even come a day when the decisions about which platform to employ and when is completely automated by machine learning algorithms trained to maximize IT infrastructure efficiency to the fullest extent possible.

In the meantime, DevOps teams should expect, to one degree or another, to revisit their workflows processes in the months ahead. Not only are more workloads being pushed out to edge computing platforms where Arm processors are commonly found, those Arm processors will soon find their way into the rest of the extended enterprise.

The last couple of years have been unlike any others. Digital transformation – be it through the establishment of remote working or the creation of new e-commerce channels – has taken place at lightning speed.

At this time of rapid change, IT teams have operated under extreme conditions. They’ve had to deal with reduced teams and tech budgets, fast-changing organisational priorities, and increased threats in terms of business continuity and information security.

As 2021 progresses, what are the elements that would help CIOs and their teams to deal with the new challenges that lie ahead? Here are five tips that we believe would help tech leaders to create a game-changing IT department.

Clear objectives from the business
Technology is still often implemented in isolation. Rather than being purchased to achieve set aims, IT is bought as a solution looking for a problem. Yet 2020 has seen a step change; IT is now seen as a problem solver. Digitisation helped companies deal with the challenges of coronavirus and now the business wants more from its tech.

With IT budgets still under downward pressure, CIOs and IT teams need direction from their business peers. They need to work in unison to create strongly-defined use cases. With data-led emerging tech, like artificial intelligence and machine learning, moving from the laboratory to the enterprise, that requirement has never been more pressing.

The good news is that CIOs and their IT teams will have spent much of the past 12 months engaging closely with their business peers to ensure that their technology demands are met. Now they must create a clear long-term digital strategy that puts the business first.

More commitment to the cloud
The past 12 months has almost been like a giant beta test for the cloud. At a moment’s notice, companies had to switch to home working, real-time collaboration and online fulfilment. It was a test that the cloud passed with flying colours.

The task for 2021 is to make even more use of the cloud. Global spending on public cloud services is forecast to grow 18.4% next year to total $304.9 billion, up from $257.5 billion in 2020, according to Gartner. The analyst says companies have more IT to do and less money to do it, so they will spend on areas such as the cloud to help accelerate digital business.

Gnarly elements of legacy IT remain in incumbent businesses of all sizes. If CIOs and their tech teams want to help their businesses change directly quickly, then they will need a cloud-first approach that allows the organisation to move with flexibility.

Agile development methodologies
Moving quickly, however, isn’t just about implementing the right technology. Without a people-centred information strategy that allows employees to exploit new opportunities collaboratively, then the promises of big data will go unfulfilled.

That’s where Agile development methodologies come in. Unlike traditional Waterfall approaches, which use a linear approach to task completion, Agile methods rely on iterative collaboration. While these techniques started in the IT department, Agile techniques have moved out of the data centre and out across all lines of business.

Smart companies use Agile techniques to bring in various stakeholders to explore how a business aim can be reached. By working together, cross-organisation teams can take small, iterative steps towards fulfilling data-led business aims in 2021.

Effective data management
Even if you’re moving towards completing business aims in an Agile manner, there’s still one area where your organisation can come unstuck: poor data management. It’s only possible to act on data if the information your business collects is consistent, accurate and secure.

Gartner says CIOs and their IT teams must as a matter of urgency prioritise spending on data management through 2021. The central role of data in the business is only going to increase, as companies rely on key performance indicators to ensure their organisation is making moves into the right market areas.

Your business will need to break down the traditional walls that define it. Rather than holding information in silos, CIOs will need to consolidate information so that decision-makers can make informed and intelligent choices.

Great account management
From the cloud through to data management, organisations needs to rely on the external partners they selects. Building trust is not necessarily straightforward, as CIOs have to use an ecosystem of IT providers with different capabilities for specific areas of provision.

With the business likely to up its demands for digital transformation through 2021, IT departments need partners that can deliver on their operational promises, rather than just hyping their offerings and selling more services when they believe the time is right.

Look for vendors that offer the highest possible level of account management. With IT budgets likely to remain constrained, tech teams need a trusted account managers that can work to help them deliver data-led digital transformation plans.

Summary
The coming months are going to involve building on some of the lessons learnt through 2020. CIOs should try to reflect, reset and reprioritise for the future.

ARB Bhd is eyeing a merger and acquisition (M&A) deal this year, leveraging its current strong net cash position, to expand the group’s cloud computing business.

The group plans to extend ARB’s footprint in the IT segment by targeting an M&A deal with a higher return on investment, which is expected to increase its shareholders’ value.

“I think we are in a good position to expand our business beyond the boundary. Over the last three years, we have solidified our position in the IT business in Malaysia.

“We have been focusing on the field of enterprise resource planning (ERP) and Internet of Things (IoT) solutions as Covid-19 accelerated user demand for cloud-based computing needs,” ARB ED Datuk Seri Larry Liew Kok Leong said in a statement yesterday.

Liew said the company aims to ride on the surge in demand for cloud-based computing as a result of the pandemic, coupled with rising interest from international clients for digitalisation and cost-optimisation efforts.

“IT solutions have been proven to help companies achieve their digitalisation efforts and improve cost optimisation in the long-term.

“We foresee a strong demand for solutions in new categories such as big data analytics, e-commerce solutions, IoT platform for 5G and data cybersecurity.

“The group needs to expand into horizontal markets to provide innovative solutions in this area, but the demand far exceeds our capacity and existing infrastructure.

“This is why the next phase for ARB is to look at M&A potentials and collaboration with our local partners,” he said.

Liew said the potential M&A and collaborations are expected to expand ARB’s geographical presence and access in new sectors while taking advantage of the cross-border business opportunities in 2021.

“Overseas expansion will help build a regional reputation for the group. On the domestic front, ARB will continue to work closely with government-linked companies, multinational corporations and public-listed companies to grow its ERP and IoT business segments,” he said.

The government recently introduced the MyDigital initiative which is intended to spur the country’s digital transformation direction plan and drive the business sector to compete globally. The blueprint targets the local digital economy to contribute 22.6% to the country’s GDP by 2026.

ARB saw its net profit for the financial year 2020 (FY20) rise to RM43.46 million compared to RM34.77 million in FY19, as its revenue more than doubled to RM219.45 million from RM102.64 million.

Why Cloud Security Reading Still Matters

Cloud environments are not secure by default. Misconfigured S3 buckets, over-permissive IAM roles, unencrypted data in transit, and weak secrets management are recurring causes of real-world breaches. Whether you run workloads on AWS, Azure, or Google Cloud Platform, understanding the underlying security principles — not just the vendor dashboards — is what separates a reactive team from a proactive one.

Books offer something documentation and blog posts rarely do: a structured, end-to-end treatment of a subject. The titles below range from vendor-neutral fundamentals to practitioner-focused guides on cloud-native security architecture. They are suitable for beginners building a foundation and for experienced engineers looking to formalise or deepen their knowledge.

What to Look for in a Cloud Security Book

Before committing time to a book, consider the following criteria:

• Publication date: Cloud services evolve rapidly. A book more than three to four years old may reference deprecated services or outdated compliance frameworks. Prioritise recent editions or books that focus on durable principles over specific console walkthroughs.
• Author credibility: Look for practitioners with demonstrable experience — security architects, penetration testers, or engineers who have built and defended production cloud environments.
• Coverage of the shared responsibility model: Any credible cloud security book should clearly explain what the cloud provider secures versus what the customer is responsible for. This model underpins everything else.
• Practical exercises: Lab-based or hands-on chapters accelerate retention and give you directly applicable skills.
• Multi-cloud or platform-specific focus: Decide whether you need a vendor-neutral treatment (useful for architects and decision-makers) or a deep dive into a specific platform (useful for engineers operating daily in AWS, Azure, or GCP).

Core Topic Areas Worth Prioritising

A well-rounded cloud security reading list should cover the following domains. Use this as a checklist when evaluating which gaps in your knowledge to fill next:

Identity and Access Management (IAM)

Excessive permissions and poorly scoped roles are among the most exploited attack surfaces in cloud environments. Books and resources in this area cover least-privilege design, role-based access control (RBAC), attribute-based access control (ABAC), federation, single sign-on (SSO), and privileged access management (PAM).

Network Security in the Cloud

Traditional perimeter-based security does not translate directly to cloud-native architectures. Core concepts include virtual private clouds (VPCs), security groups, network access control lists (NACLs), private endpoints, zero-trust networking, and micro-segmentation.

Data Protection and Encryption

Encryption at rest and in transit, key management services (KMS), secrets management tools (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault), and data classification frameworks are essential knowledge for anyone handling sensitive workloads in the cloud.

Compliance and Governance

Regulatory requirements such as GDPR, HIPAA, PCI-DSS, and SOC 2 impose specific technical and organisational controls. Understanding how cloud services map to these frameworks — and where responsibility remains with the customer — is critical for compliance-driven teams.

Incident Response and Threat Detection

Cloud environments generate large volumes of logs and telemetry. Books in this area cover SIEM integration, cloud-native logging services (AWS CloudTrail, Azure Monitor, GCP Cloud Audit Logs), threat detection, and incident response playbooks tailored to cloud scenarios.

DevSecOps and Secure CI/CD

Shifting security left means integrating static analysis, dependency scanning, container image scanning, and infrastructure-as-code (IaC) policy checks into the development pipeline. This is increasingly a required competency for platform and DevOps engineers, not just security specialists.

Building Your Reading List: A Suggested Approach

Rather than reading exhaustively, map books to your current role and the most pressing security risks in your environment:

1. Assess your knowledge gaps. Use a framework such as the CSA Cloud Controls Matrix or the MITRE ATT&CK Cloud matrix to identify where your understanding is weakest.
2. Match books to gaps. Select titles that directly address those areas rather than reading generalist books cover to cover.
3. Combine reading with practice. Set up a personal cloud lab (free-tier accounts on AWS, Azure, or GCP are sufficient for most exercises) to apply concepts as you read.
4. Revisit titles as services evolve. Check whether newer editions exist before starting a book you last read two or more years ago.

Supplementary Resources Beyond Books

Books are one component of a broader learning strategy. Pair your reading with:

• Vendor security documentation: AWS Well-Architected Framework Security Pillar, Microsoft Azure Security Benchmark, and Google Cloud Security Foundations Blueprint are all freely available and regularly updated.
• Cloud Security Alliance (CSA) publications: The CSA publishes guidance documents, research papers, and the Cloud Controls Matrix at no cost.
• Certification study materials: Certifications such as AWS Certified Security – Specialty, CCSP (Certified Cloud Security Professional), and CompTIA Cloud+ come with structured syllabi that complement book-based learning.
• Threat intelligence feeds and breach post-mortems: Reading real-world incident reports (many published by cloud vendors and security firms) reinforces the practical relevance of what you read in books.

Final Thoughts

Cloud security is not a one-time certification or a single book. It is an ongoing discipline that requires continuous learning as attack surfaces shift and cloud providers introduce new services. Building a deliberate reading habit — even one chapter per week — compounds into significant expertise over time. Start with the domain most relevant to your current work, apply what you learn in a lab environment, and expand from there.

If your organisation lacks in-house cloud security expertise, consider evaluating managed security service providers (MSSPs) with proven cloud practices as a complementary measure while your team builds capability.

What Is IT Infrastructure Monitoring Software?

IT infrastructure monitoring software gives operations and DevOps teams continuous visibility into the health, performance, and availability of servers, networks, storage, virtual machines, containers, and cloud services. By collecting metrics, logs, and events from across the stack, these tools enable teams to detect anomalies early, troubleshoot faster, and demonstrate compliance with service-level agreements (SLAs).

Demand for these tools has accelerated alongside the growth of hybrid cloud environments, microservices architectures, and remote-first operations—all of which increase the complexity that teams must monitor and manage.

Market Overview and Growth Drivers

According to the referenced market research report (covering the period 2020–2026), the IT infrastructure monitoring software market was analysed using key metrics including Compound Annual Growth Rate (CAGR), market share by region, and competitive positioning. While the precise figures are proprietary, analysts broadly agree that several structural forces are driving sustained growth in this sector:

• Hybrid and multi-cloud adoption: Enterprises running workloads across on-premises data centres and multiple cloud providers require unified monitoring platforms that can aggregate telemetry from all environments.
• Rise of DevOps and SRE practices: Observability is now a first-class concern in modern software delivery. Teams demand monitoring tools that integrate into CI/CD pipelines and support on-call workflows.
• Regulatory and compliance requirements: Industries such as finance and healthcare need audit trails and uptime guarantees that monitoring platforms help enforce.
• Growth in edge computing and IoT: Expanding infrastructure footprints beyond centralised data centres create new monitoring surface areas.
• Increased frequency of cyber threats: Anomaly detection capabilities built into monitoring tools increasingly double as a first line of security awareness.

Key Vendors in the Market

The report identifies the following companies as significant players in the IT infrastructure monitoring software market:

• Zabbix – Open-source platform with strong network and server monitoring capabilities, popular in enterprises that prefer self-hosted deployments.
• Datadog – Cloud-native SaaS observability platform covering infrastructure, APM, logs, and security in a single pane of glass.
• Nagios – One of the earliest open-source monitoring tools, widely deployed for network and host monitoring with a large plugin ecosystem.
• VMware – Provides infrastructure monitoring integrated with its virtualisation and cloud management portfolio (e.g., vRealize Operations).
• Paessler (PRTG) – Known for network monitoring with a sensor-based licensing model suited to mid-market organisations.
• SolarWinds – Broad IT management portfolio including infrastructure monitoring (Orion platform) aimed at IT operations teams.
• Microsoft – Azure Monitor and System Center Operations Manager (SCOM) serve customers monitoring Microsoft-centric and hybrid environments.
• Micro Focus – Enterprise IT operations management tools, including Operations Bridge for hybrid IT monitoring.
• ManageEngine – Offers a range of affordable IT management and monitoring tools targeted at SMBs and mid-enterprise.
• Progress (WhatsUp Gold) – Network and infrastructure monitoring with a focus on ease of deployment.
• Icinga – Open-source fork of Nagios with a modern API-driven architecture and active community.
• OpsRamp – AIOps and hybrid infrastructure monitoring platform acquired by HPE.
• NetApp – Storage-centric monitoring and cloud data services management.
• IBM – Enterprise-scale monitoring via IBM Instana, IBM Tivoli, and the broader Watson AIOps portfolio.

Market Segmentation

The research segments the market across several dimensions to give buyers and vendors a clearer picture of where demand concentrates:

By Product Type

Solutions range from dedicated network performance monitors (NPM) and server monitoring agents to full-stack observability platforms that combine infrastructure metrics, application performance management (APM), log management, and synthetic monitoring in a single tool.

By Deployment Model

Buyers can choose between on-premises installations (self-hosted open-source or licensed), SaaS/cloud-delivered platforms, or hybrid arrangements. SaaS has gained share rapidly because it eliminates the operational overhead of maintaining the monitoring infrastructure itself.

By End User / Application

Primary buyers include IT operations teams, network operations centres (NOCs), DevOps and SRE teams, managed service providers (MSPs), and cloud operations groups. Verticals with heavy usage include banking and financial services, healthcare, telecommunications, retail, and government.

By Distribution Channel

Vendors reach buyers through direct sales, cloud marketplaces (AWS Marketplace, Azure Marketplace, Google Cloud Marketplace), VAR/reseller channels, and managed service partnerships.

Regional Outlook

The report analyses market contribution by geography. At a high level, the global picture during the 2020–2026 forecast window was expected to follow patterns consistent with broader enterprise software trends:

• North America – Historically the largest market, driven by high cloud adoption rates, large enterprise IT budgets, and a concentration of major vendors.
• Europe – Significant market with growing compliance-driven monitoring demand (GDPR, NIS Directive). Strong adoption in Germany, UK, and the Nordics.
• Asia-Pacific – Fastest-growing region during the forecast period, fuelled by rapid digitalisation in India, China, and Southeast Asia.
• Latin America, Middle East and Africa – Emerging markets with growing IT infrastructure investments and increasing cloud adoption.

Market Dynamics: Drivers and Restraints

Growth Drivers

• Increasing complexity of hybrid and multi-cloud IT environments
• Adoption of AIOps to reduce alert fatigue and automate root-cause analysis
• Growing importance of observability as a DevOps practice
• Expansion of remote work increasing reliance on well-monitored infrastructure
• Regulatory requirements mandating uptime guarantees and audit logging

Market Restraints

• High total cost of ownership for on-premises deployments
• Complexity of tool sprawl—many organisations run multiple overlapping monitoring solutions
• Skills shortage in IT operations and observability engineering
• Integration challenges when monitoring heterogeneous environments with legacy systems
• Data privacy concerns when routing telemetry to third-party SaaS platforms

Analytical Frameworks Covered in the Research

The market research report applies standard industry-analysis frameworks to structure its findings:

• Porter’s Five Forces – Assesses competitive rivalry, threat of new entrants, bargaining power of buyers and suppliers, and threat of substitutes.
• Value Chain Analysis – Maps how value is created and distributed across vendors, channel partners, and end users.
• CAGR and Revenue Forecasting – Quantifies expected market growth over the forecast period.
• Competitive Landscape and Company Profiles – Profiles key vendors by revenue, product portfolio, strategy, and recent developments.

Choosing an IT Infrastructure Monitoring Tool: Practical Considerations

For DevOps engineers and IT operations managers evaluating tools in this space, the following criteria are worth weighing independently of any market report:

• Coverage breadth: Does the tool monitor all layers of your stack—network, compute, storage, containers, Kubernetes, serverless, and SaaS applications?
• Integration with your toolchain: Look for native integrations with your alerting (PagerDuty, Opsgenie), ticketing (Jira, ServiceNow), and incident management workflows.
• Scalability: Can the platform handle your metric cardinality, retention requirements, and ingestion volume at acceptable cost?
• Open standards support: Platforms that support OpenTelemetry, Prometheus, and StatsDB reduce vendor lock-in.
• Total cost of ownership: SaaS pricing models can escalate quickly at scale. Compare per-host, per-metric, and data-volume pricing models carefully.
• Alerting quality: Evaluate noise suppression, anomaly detection, and correlation features to avoid alert fatigue.

About the Research Source

The underlying market research was published by Reports And Markets, part of the Algoro Research Consultants group, which produces market intelligence reports across technology and industrial sectors. The original summary was published via bulletinline. The full report, including precise CAGR values, revenue forecasts, and detailed company profiles, is a paid commercial document available through the publisher.

What Is Web Monitoring Software?

Web monitoring software tracks the availability, performance, and behavior of websites, web applications, APIs, and mobile applications. It gives operations and development teams visibility into how their services are performing in real time, helps identify regressions after deployments, and surfaces infrastructure-level issues before they reach end users.

Modern platforms in this category have evolved well beyond simple uptime checks. They now encompass full-stack observability, application performance monitoring (APM), infrastructure monitoring, distributed tracing, log aggregation, and real-user monitoring (RUM).

Major Vendors in the Web Monitoring Space

Several well-established vendors dominate this market. Each has a distinct approach, pricing model, and area of strength. Understanding the differences helps teams make informed procurement and architecture decisions.

LogicMonitor

LogicMonitor is a SaaS-based infrastructure and network monitoring platform aimed primarily at managed service providers (MSPs) and enterprise IT operations. It offers agentless monitoring for on-premises and cloud infrastructure, with a strong focus on automated device discovery and alerting. It integrates with common DevOps toolchains and ITSM platforms.

AppDynamics

AppDynamics, now part of Cisco, is an APM and business observability platform. It provides deep code-level visibility into application performance and correlates technical metrics with business outcomes (such as transaction revenue or conversion rates). AppDynamics is widely used in large enterprise environments running Java, .NET, Node.js, and other supported runtimes.

New Relic

New Relic offers a broad observability platform covering APM, infrastructure monitoring, distributed tracing, browser monitoring, and synthetic testing. It has transitioned to a consumption-based pricing model and positions itself as an all-in-one observability tool for teams of all sizes, from startups to enterprises.

Dynatrace

Dynatrace differentiates itself with AI-assisted root cause analysis through its Davis AI engine. It automatically discovers and maps dependencies across cloud-native environments, containers, and microservices. Dynatrace is particularly strong in Kubernetes and cloud-native observability, and is frequently chosen by enterprises undergoing digital transformation.

SolarWinds

SolarWinds offers a wide portfolio of IT management and monitoring tools, including network performance monitoring, server and application monitoring, and web performance monitoring. It is well-known in network operations and IT operations center (ITOC) environments. SolarWinds products are available both as on-premises installations and SaaS offerings.

SmartBear

SmartBear is focused primarily on API quality and testing tooling, including AlertSite for web and API monitoring. It is widely used by QA engineers and developers who need to combine functional testing with performance and availability monitoring across web services and APIs.

Deployment Models

Web monitoring platforms are typically delivered through one of the following deployment models, each with trade-offs in terms of setup complexity, data residency, and total cost of ownership:

• SaaS-based: The vendor hosts and manages the monitoring infrastructure. Teams instrument their applications with agents or SDKs and data is sent to the vendor’s cloud. This model minimizes operational overhead and enables rapid onboarding. LogicMonitor, New Relic, and Dynatrace all offer fully SaaS-delivered options.
• Website and application monitoring: Covers synthetic monitoring (scripted transactions from external probes), real-user monitoring (RUM from actual browser sessions), and uptime checks. Suitable for public-facing properties where external availability and user experience are the primary concern.
• Mobile app monitoring: Extends observability to iOS and Android applications through mobile SDKs that capture crash reports, network request performance, and user interaction traces.

Key Use Cases and End-User Segments

The buyers and users of web monitoring software generally fall into two broad categories:

Enterprise IT

Large organizations use monitoring platforms to maintain service-level agreements (SLAs), support ITIL-aligned change and incident management, and provide visibility across hybrid or multi-cloud environments. Compliance requirements, role-based access control, and integrations with ITSM tools like ServiceNow or PagerDuty are common procurement requirements in this segment.

Developers and Operations Teams

DevOps and SRE teams use monitoring software as a core part of their continuous delivery pipelines. Key requirements include CI/CD integration, deployment markers, anomaly detection, and the ability to correlate code changes with performance regressions. Developers increasingly want self-serve access to observability data without routing requests through a central ops team.

Choosing the Right Platform: What to Evaluate

When evaluating web monitoring software for your organization, consider the following factors:

• Coverage: Does the platform cover your full stack—infrastructure, application, browser, mobile, and third-party APIs?
• Instrumentation overhead: How much effort is required to instrument services, and what is the performance impact of agents or SDKs?
• Pricing model: Per-host, per-user, consumption-based, or flat-fee pricing each suits different team sizes and usage patterns.
• Alerting and on-call integration: Does the platform integrate with your existing incident management tools (PagerDuty, Opsgenie, Slack)?
• AI and automation: Some platforms (notably Dynatrace and AppDynamics) offer automated root cause analysis, which can reduce mean time to resolution (MTTR) in complex microservices environments.
• Data retention and compliance: For regulated industries, check where data is stored, how long it is retained, and what export or audit capabilities are available.
• Open standards support: Platforms that support OpenTelemetry allow teams to avoid vendor lock-in and reuse instrumentation across different backends.

Regional Adoption Trends

Demand for web monitoring software is global, with adoption driven by digital transformation initiatives, cloud migration, and the proliferation of e-commerce and SaaS businesses. North America and Europe have historically had the highest concentrations of enterprise monitoring deployments, while Asia-Pacific is a fast-growing region driven by expanding cloud infrastructure investment and a growing base of software-intensive businesses.

Summary

Web monitoring software is no longer a niche tool—it is a critical part of how engineering and operations teams deliver reliable digital services. Whether you are evaluating full-stack APM platforms like Dynatrace or AppDynamics, infrastructure-focused tools like LogicMonitor and SolarWinds, or developer-centric observability platforms like New Relic, aligning your choice with your team’s workflows, technology stack, and compliance requirements will deliver the most value. As the space continues to evolve toward AI-assisted observability and OpenTelemetry-based standardization, the platforms that offer flexibility alongside depth of insight will be the ones best positioned for long-term adoption.

How Kubernetes Works

Kubernetes sits on top of an operating system and manages pods of containers running across nodes in a cluster. An administrator sends commands to the Kubernetes master, which in turn instructs worker nodes to schedule and run workloads. The master automatically determines which node is best suited for each task based on available resources, then allocates compute capacity and assigns pods accordingly. This abstraction gives operators high-level control over containerized applications without manually managing individual containers or nodes.

Kubernetes Architecture: Core Concepts

Before running your first cluster, it helps to understand the key building blocks of Kubernetes and how they relate to one another.

Cluster

A Kubernetes cluster is the top-level unit. It always requires at least one master node running the control-plane components that handle scheduling, workload management, and cluster communications. For production environments, control-plane services are typically distributed across multiple machines for redundancy.

Kubernetes Master (Control Plane)

The master node runs three core processes:

• kube-apiserver — Validates and configures data for API objects such as pods, services, and replica sets. All communication with the cluster goes through this component.
• kube-scheduler — Watches for newly created pods with no assigned node and selects a node for them to run on based on resource requirements and constraints.
• kube-controller-manager — Runs controller loops that regulate the state of the cluster, including node controllers, replication controllers, and endpoint controllers.

Pods

A pod is the smallest deployable unit in Kubernetes. It is a group of one or more containers that share the same network namespace and storage volumes. Containers within a pod are always scheduled together on the same node and communicate with each other over localhost. Pods are ephemeral by design — if a pod dies, Kubernetes replaces it rather than restarting the original.

Services

Because pods are short-lived, their IP addresses change frequently. A Service provides a stable network endpoint — a logical grouping of pods with a consistent DNS name and IP address. Other parts of the application send requests to the Service rather than individual pods, and Kubernetes handles routing to healthy pod instances automatically.

kube-proxy

kube-proxy runs on every node and maintains network rules that allow communication to pods from inside or outside the cluster. It handles basic load balancing across pod instances backing a Service and ensures that cluster networking remains accessible and isolated as required.

Volumes

A Kubernetes Volume provides persistent or shared storage to all containers within a pod. Unlike a container’s ephemeral filesystem, data written to a volume survives container restarts within the pod’s lifetime. The volume is only removed when the pod itself is deleted. A single pod can use multiple volumes of different types simultaneously.

Replica Sets

A ReplicaSet ensures that a specified number of identical pod instances are running at all times. It uses a label selector to identify the pods it manages. If a pod fails or is deleted, the ReplicaSet automatically creates a replacement, providing high availability and redundancy.

Nodes

A node is a worker machine — physical or (more commonly) virtual — that runs pods. Each node includes:

• kubelet — The primary agent on each node. It watches for pod specifications assigned to its node and ensures containers are running in the desired state.
• kube-proxy — Handles network routing and load balancing at the node level.

Ways to Set Up and Run Kubernetes

There are multiple paths to getting a Kubernetes environment running, ranging from a local single-node setup for learning to fully managed cloud services for production workloads.

Minikube (Local Development)

Minikube runs a single-node Kubernetes cluster inside a virtual machine on your local workstation. It supports Linux, macOS, and Windows, making it an ideal starting point for learning Kubernetes concepts without needing cloud resources. After installing Minikube and a hypervisor or container runtime, you can launch a cluster with a single command:

minikube start

From there, you can deploy sample applications, experiment with kubectl commands, and explore the Kubernetes API in a low-risk local environment.

Katacoda (Browser-Based Practice)

Katacoda offers interactive, browser-based Kubernetes scenarios that require no local installation. Users sign in, select a scenario, and work inside a temporary terminal environment that is recycled when the session ends. This is a quick way to run through guided exercises without configuring any local tooling. Note that Katacoda’s future availability may vary, so verify its current status before relying on it for training programs.

Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is a fully managed Kubernetes service on Google Cloud Platform. It handles control-plane management, upgrades, and scaling automatically. GKE integrates with GCP services such as Cloud Load Balancing, Cloud Monitoring, and Cloud Logging. Users can create a free-tier account and spin up a multi-node cluster quickly through the GCP Console or via the gcloud CLI.

Azure Kubernetes Service (AKS)

Azure Kubernetes Service (AKS) provides managed Kubernetes on Microsoft Azure with no control-plane costs — you pay only for the worker nodes. AKS integrates natively with Azure DevOps, Azure Monitor, Azure Active Directory for RBAC, and Visual Studio Code Kubernetes tooling. It supports both Linux and Windows node pools, making it practical for mixed workload environments.

Amazon Elastic Kubernetes Service (EKS)

Amazon Elastic Kubernetes Service (EKS) is a managed service that runs Kubernetes on AWS without requiring you to operate a control plane. EKS supports both Linux and Windows containers and integrates with AWS services including IAM, VPC networking, CloudWatch, and Elastic Load Balancing. A free-tier AWS account can be used to experiment, though EKS itself incurs a per-cluster hourly charge — verify current AWS pricing before starting.

Kubernetes Dashboard

The Kubernetes Dashboard is a web-based UI for cluster management. It allows you to view resource utilization, deploy containerized applications, inspect running workloads, and troubleshoot issues — all from a browser. The Dashboard is not installed by default and must be deployed separately using kubectl apply with the official manifest. After deployment, access is typically set up through kubectl proxy to avoid exposing the Dashboard publicly without authentication.

kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.x.x/aio/deploy/recommended.yaml
kubectl proxy

Replace v2.x.x with the current stable release version from the official Kubernetes Dashboard GitHub repository before running this command.

Why Use Kubernetes?

As applications grow and container counts increase across many servers, manual operations become impractical. Kubernetes addresses this by providing:

• Automated scheduling — Places containers on nodes based on resource availability and constraints.
• Self-healing — Automatically restarts failed containers, replaces terminated pods, and reschedules workloads from failed nodes.
• Horizontal scaling — Scales applications up or down based on CPU, memory, or custom metrics.
• Service discovery and load balancing — Exposes containers via DNS or IP and distributes traffic automatically.
• Rolling updates and rollbacks — Deploys new application versions incrementally and reverts to a previous state if something goes wrong.
• Secret and configuration management — Stores and manages sensitive data and configuration separately from application images.

These capabilities make Kubernetes the de facto standard for running containerized microservices in production, providing the reliability and automation that modern application architectures require.

Next Steps

If you are new to Kubernetes, start with Minikube or a browser-based environment like Katacoda to learn the basics of pods, services, and deployments without incurring cloud costs. Once comfortable, move to a managed cloud service — GKE, AKS, or EKS — to experience production-grade cluster management. The official Kubernetes documentation is the most reliable and up-to-date reference as you progress.

DevOps Has Grown Up — Now What?

The phrase “DevOps 2020” is a milestone marker. The term — and the culture and practices it represents — has long shed its “new and shiny” status in IT. That doesn’t make DevOps passé; it means it has become pervasive. As organizations at every stage of the adoption curve continue to mature, understanding where the practice is heading matters as much as knowing where it started.

“In 2020, we will see organizations [continue to] embrace DevOps practices at all levels,” says Tom Iverson, Managing Principal, Cloud and DevOps at RBA.

By “levels,” Iverson is not referring to organizational hierarchies. He means that every team’s starting point with DevOps was different — including when they started. More advanced teams may be innovating and optimizing their continuous delivery pipelines, while organizations earlier in the adoption curve might still be experimenting with basic automation or working through the cultural changes that DevOps demands.

Regardless of where your organization sits on that spectrum, the following four trends are worth understanding.

Trend 1: DevOps Maturity Fuels Continued Interest in Cloud-Centric Tools

As DevOps practices mature, they naturally drive adoption of cloud-native technologies. The relationship is bidirectional: DevOps culture pushes teams toward better tooling, and better tooling reinforces the cultural and process improvements that DevOps demands.

One technology stands out as a clear example of this dynamic: Kubernetes.

“In 2020 we expect to see the continued and increased adoption of Kubernetes for organizations to deploy services. The combination of its ease of use, depth of features, and integration with major cloud platforms will make it an increasingly appealing choice for developing and deploying software — even more so if your organization already deploys containerized software.”

— Josh Komoroske, Senior DevOps Engineer, StackRox

Beyond container orchestration, cloud technologies more broadly are shifting how governance and self-service are handled inside IT organizations. Heavy, manual governance models are giving way to lighter, automation-driven custodial approaches.

“Cloud technologies will continue to grow and a ‘DevOps first’ mindset will be baked into new developments. Heavy-handed governance models will turn to more of a custodial approach due to the amount of automation available in the cloud space. For example, IT will look to provide self-service options to support rapid provisioning of temporary resources for proof-of-concept work.”

— Tom Iverson, Managing Principal, Cloud and DevOps, RBA

Iverson also noted that commodity tools — office suites, low-code platforms, and ChatOps solutions — would increasingly serve as the interface for that self-service experience, bringing DevOps-style capabilities directly to teams where they already work.

What This Means Practically

• If your team hasn’t yet invested in Kubernetes, the ecosystem maturity and managed Kubernetes offerings from major cloud providers lower the barrier significantly.
• Self-service infrastructure provisioning — backed by guardrails rather than gatekeeping — reduces bottlenecks and accelerates experimentation.
• ChatOps integrations (e.g., triggering deployments or querying system status from a Slack channel) bring operations closer to the teams that need visibility.

Trend 2: IT Leaders Are Revisiting DevOps Success Metrics

Measurement has always been central to DevOps. Metrics like mean time to deploy (MTTD), deployment frequency, and change failure rate gave teams concrete ways to demonstrate improvement. But as DevOps matures, organizations are questioning whether those operational metrics tell the full story.

“We’ve learned that faster is not always better when it comes to software release cycles. As a result, we’re seeing organizations pivoting away from release cadences and emphasizing business value and customer satisfaction.”

— Justin Donato, VP of IT, Nintex

This shift represents an intersection between DevOps and broader digital transformation goals. The original pitch for DevOps — “we’ll ship faster and more frequently” — was a means to an end, not the end itself. Organizations are now asking the harder follow-up question: does shipping faster actually benefit the business?

Donato points to accelerated sales cycles and time-to-revenue as examples of concrete, business-level outcomes that DevOps can and should be measured against.

“Businesses today aren’t looking so much for fast releases as for quality releases. Competitors can spring up overnight, so it’s key to deliver capabilities that keep your customers satisfied and loyal; you don’t want them looking elsewhere.”

— Justin Donato, VP of IT, Nintex

Rethinking Your Metrics Dashboard

If your DevOps scorecard is still primarily composed of pipeline throughput metrics, consider adding or elevating:

• Customer satisfaction scores (CSAT/NPS) — Do releases actually improve the user experience?
• Time to revenue — How quickly does a shipped feature generate measurable business return?
• Change failure rate combined with user-reported defects — Speed means little if quality suffers.
• Feature adoption rates — Are users engaging with what you’re shipping?

The goal is not to replace operational metrics but to connect them to outcomes your executive team and customers actually care about.

Key Takeaways

DevOps is no longer a movement — it’s a baseline expectation in modern software organizations. The trends highlighted here reflect a broader maturation: teams are moving from “are we doing DevOps?” to “are we doing DevOps well, and does it matter to the business?”

• Cloud-native tools, particularly Kubernetes, are consolidating their position as core DevOps infrastructure.
• Self-service and automation are replacing manual governance as the primary IT control model.
• Business-value metrics are increasingly important alongside traditional operational measurements.
• Quality and customer satisfaction are competing with — and sometimes overtaking — release velocity as primary success indicators.

Whether your team is just beginning its DevOps journey or refining a well-established practice, these themes provide a useful frame for evaluating priorities and investments.

VMware’s Strategic Bet on India

Enterprise software major VMware has identified India’s ongoing digitisation drive as one of its most significant growth opportunities in the Asia-Pacific region. Speaking to IANS, David Wakeman, VMware’s Senior Director for End User Computing, Asia Pacific and Japan, outlined how the company is positioning itself to support both large enterprises and government bodies as they extend digital services to a broader population.

The California-headquartered company had, at the time of the interview, announced a $2 billion investment in India over five years, earmarked for expanding facilities, growing headcount, and accelerating research and development activity in the country.

Where VMware Is Already Working in India

VMware had already embedded its technology inside some of India’s most prominent organisations by 2019, including:

• State Bank of India (SBI) – one of the world’s largest retail banks by customer base
• Bharti Airtel – a major telecommunications provider operating across India and Africa
• BookMyShow – a high-traffic consumer ticketing and entertainment platform

Wakeman highlighted work with Indian banks around workforce inclusion policies — specifically, enabling customers to conduct financial transactions via mobile devices rather than requiring a physical branch visit. He also noted conversations with insurance companies exploring how field and office staff can be empowered through digital tooling.

Workspace ONE: Delivering Any App on Any Device

A central part of VMware’s pitch to Indian enterprises is its Workspace ONE unified endpoint management (UEM) platform. The platform integrates three capabilities in a single solution:

• Access control – identity-aware, conditional access policies
• Application management – lifecycle management for native, web, and virtual applications
• Multi-platform endpoint management – covering iOS, Android, Windows, macOS, and Chrome OS devices

Workspace ONE is available both as a cloud-hosted SaaS service and as an on-premises deployment, giving organisations flexibility based on regulatory requirements or infrastructure preferences. Wakeman noted that VMware operates over 1,000 data centres globally for the platform and established a dedicated data centre in India approximately three years before the interview to support local SaaS delivery.

People Must Remain at the Centre of Digitisation

A recurring theme in Wakeman’s remarks was the risk of treating digitisation as a purely technical exercise. He was direct on this point:

“If we just look at digital for digital’s sake and miss the people a bit, that’s a problem. We’re spending a lot of time thinking about how technology and employee experience can come together.”

David Wakeman, Senior Director, End User Computing, APJ – VMware

He encouraged organisations embarking on digital transformation programmes to evaluate three dimensions beyond the technology itself:

• Employee experience – does the new tooling make it easier or harder for staff to do their jobs?
• Cultural access – does it open new pathways for under-represented groups to enter or remain in the workforce?
• Practical constraints – does it reduce burden on workers, including logistical challenges such as commuting or travel to physical locations?

India’s population of technology-savvy consumers, Wakeman argued, makes it an ideal early proving ground for new capabilities. “Indians are avid consumers of technology, so whenever we do something new or different, immediately you will see that it will show up here in India first,” he said.

Why Cloud Adoption in India Has Lagged Other APJ Markets

Despite enthusiasm for digital tools, Wakeman acknowledged that enterprise cloud adoption in India had been slower than in comparable markets such as Australia and Japan. He attributed this to two structural factors:

1. Regulatory and Compliance Constraints

Government agencies and regulated sectors such as banking have historically faced stricter compliance requirements around data residency and sovereign control. While markets like Australia and Japan had already seen more conservative government and financial-sector organisations begin migrating workloads to cloud, India’s equivalent sectors remained more cautious at the time of this interview.

2. The Cost Perception Problem

The second barrier was a widespread assumption that cloud should be cheaper than on-premises infrastructure. As Wakeman explained:

“Cloud is not necessarily cheaper than what people can do themselves on premise. The value proposition initially around what Cloud offers was first approached by the Indian market as a lower cost way than us running it, and it’s taken some time for the majority of clients to change and think about Cloud having a different set of value and property than just about the infrastructure being lesser.”

David Wakeman, VMware

This is a shift in framing that cloud vendors and their channel partners continue to navigate globally: moving the conversation from cost reduction to agility, scalability, and speed of innovation as the primary value drivers.

Key Takeaways for DevOps and IT Leaders

• Large-scale digitisation programmes succeed when employee experience is treated as a first-class outcome, not an afterthought.
• Regulatory compliance remains a significant brake on cloud adoption in government and financial sectors — on-premises and hybrid deployment options are not going away.
• The business case for cloud needs to be articulated in terms of agility and innovation velocity, not just infrastructure cost savings.
• Mobile-first approaches to service delivery (banking, insurance, government) are proving particularly impactful in markets with large under-served populations.
• Data residency — having a local cloud region or data centre — is often a prerequisite for regulated industries considering SaaS adoption.

Original reporting by IANS, as published by Telangana Today (December 2019).

The Decade Open Source Won

If the 2000s were the years when open source fought for survival against incumbent proprietary vendors, the 2010s were the decade when open source decisively won — and began driving the majority of meaningful technological innovation. From cloud infrastructure to mobile platforms, from big data pipelines to data science toolkits, open source sat at the center of every major technology trend between 2010 and 2020. That dominance even drew contributions from some of its most historically hostile opponents.

GitHub: The Social Layer That Changed Everything

“GitHub changed everything… Nothing else [comes] close [in importance],” declared Red Hat’s Andrew Shafer. Git itself has existed since 2005, but GitHub — founded in 2008 — made distributed version control accessible to a mass audience. Previous platforms like SourceForge and Google Code had hosted open source projects, but GitHub redefined how developers collaborated on them.

The key differentiator was social infrastructure. As Cloud CMS founder Michael Uzquiano observed, “the facility of pull requests via systems like GitHub… really delivered on the promise of code being open.” A pull request is not merely a technical artifact — it is a conversation between contributors. Hazelcast’s David Brimley extended this point, arguing that fully integrated tooling including wikis, Actions, and CI systems enabled distributed open source teams to grow in ways that raw version control alone never could. Open source became open collaboration, and that shift compounded throughout the decade.

Microsoft’s Unlikely Transformation

The developer world held its breath in mid-2018 when Microsoft announced it had acquired GitHub for $7.5 billion. A decade earlier, such a deal would have seemed absurd. Microsoft under Steve Ballmer had famously called Linux a “cancer” and open source “un-American.” By 2010, the company’s developer relationships were badly strained.

Under CEO Satya Nadella, Microsoft’s posture shifted dramatically. By the close of the decade, Microsoft had become one of the largest corporate contributors to open source projects on GitHub, measured by the number of employees actively contributing. This was not purely altruism — Microsoft is a developer-platform company, and aligning with where developers actually worked was a matter of commercial survival. The GitHub acquisition ultimately reinforced rather than undermined that community.

Cloud: The Infrastructure That Completed Open Source

Disclosure in the original article: author Matt Asay joined AWS in August 2019.

Cloud computing underpins nearly every open source trend of the 2010s. Without cloud, there would be no GitHub at scale, no modern CI/CD toolchains, and no dramatic rise of containers as a deployment primitive. Cloud gave developers the ability to spin up compute resources on demand, without routing requests through Purchasing or waiting for IT to provision physical servers. That self-service model mirrored the accessibility of open source software itself: both removed gatekeepers from the path between a developer and a working system.

Tim O’Reilly anticipated this complementary relationship as early as 2008. Through the 2010s it became concrete reality: cloud accelerated the adoption of open source, and open source projects — Linux, Kubernetes, Kafka, Spark, and dozens of others — became the foundational components of every major cloud platform.

The Uneasy Alliance: Cloud vs. Commercial Open Source Vendors

The cloud-open source relationship was not always harmonious. Throughout the late 2010s, commercial open source vendors grew concerned that hyperscale cloud providers were offering managed versions of their software without contributing meaningfully back to the projects. Several vendors — including MongoDB, Redis Labs, Confluent, and Elastic — experimented with license changes designed to restrict cloud providers from distributing their software as a service. These moves generated significant community debate about the boundaries of open source licensing.

At the same time, many of those same vendors reported to investors that cloud competition had not materially affected their download numbers, community adoption, or sales metrics — a tension that remained unresolved as the decade closed.

A Cambrian Explosion: Front-End, Containers, and Beyond

Against this infrastructure backdrop, the volume of open source innovation across every layer of the stack was extraordinary. Enterprise conversations often fixate on Kubernetes and containers, but front-end open source frameworks touched a far larger population of working developers. To illustrate the scale difference, AWS’s Ian Massingham highlighted GitHub star counts and repository numbers around the close of the decade:

• Kubernetes: ~60,200 stars; ~43,600 repositories
• Vue: ~152,000 stars; ~324,000 repositories
• React: ~140,000 stars; 1 million+ repositories
• Node.js: ~65,800 stars; ~746,000 repositories
• Angular: ~54,300 stars; ~672,000 repositories

These figures are point-in-time snapshots from around 2019 and will have changed substantially since. They illustrate relative developer engagement at that moment, not current rankings.

The JavaScript Framework Wars

Diffblue CEO Mathew Lodge described the JavaScript ecosystem as a “brutal, feral space” — and the description holds up. Throughout the decade, every time React, Angular, or another framework appeared to consolidate dominance, a new challenger emerged. The pattern reinforced a broader rule: any new programming language or framework that wanted serious adoption had to be open source from day one. Even Apple, historically cautious about open source, released its Swift language under an open source license in 2015.

Data Infrastructure: From Hadoop to Spark and Beyond

The open source data infrastructure space evolved just as rapidly. Apache Hadoop defined “big data” in the early part of the decade, only to be superseded in many workloads by Apache Spark. Spark itself was joined by a proliferating ecosystem of streaming, orchestration, and storage projects. RedMonk analyst James Governor argued that software development was entering a polyglot era — multiple languages, frameworks, and data stores coexisting rather than one standard dominating — and the data infrastructure world confirmed that thesis thoroughly.

Databases: NoSQL Proliferation and the PostgreSQL Renaissance

For decades, relational databases from a handful of enterprise vendors dominated data storage. The late 2009 launch of MongoDB marked a turning point. Faced with the volume, variety, and velocity of modern data, developers began adopting a wide array of NoSQL databases: document stores, key-value stores, graph databases, time series databases, and more — nearly all of them open source.

Alongside this explosion of new options, PostgreSQL staged a quiet resurgence. Long overshadowed by MySQL among open source relational databases, PostgreSQL steadily accumulated features — JSON support, advanced indexing, extensibility — that made it competitive with commercial databases in enterprise workloads. By the close of the decade, DB-Engines rankings placed PostgreSQL among the top four most popular databases overall, a ranking few would have predicted in 2010.

PostgreSQL’s trajectory illustrates one of open source’s underappreciated strengths: the ability of a mature project to evolve continuously to meet new use cases, rather than being replaced by something entirely new. Linux has demonstrated the same resilience in operating systems. The decade that produced an astonishing volume of new open source projects also confirmed that well-governed, community-driven projects can have remarkably long productive lives.

Key Takeaways for DevOps Engineers

• GitHub’s pull request model transformed open source from code sharing into collaborative software engineering at scale.
• Cloud and open source are mutually reinforcing — each accelerated the other’s adoption throughout the decade.
• License tensions between open source vendors and cloud providers are a structural challenge, not a passing dispute, and remain relevant to any team evaluating open source dependencies.
• Front-end open source ecosystems (React, Vue, Angular, Node.js) reach more working developers than infrastructure projects like Kubernetes, even if infrastructure gets more conference keynote time.
• The polyglot era is real: choosing the right tool for each layer of a system, rather than standardizing on one language or database, reflects how production systems actually get built.
• PostgreSQL’s resurgence is a reminder to evaluate mature open source projects on their current capabilities, not their historical reputation.

The Alert Management Problem in DevOps

Modern DevOps teams depend on continuous monitoring to keep systems performant and reliable. But no engineer is going to stare at a live dashboard around the clock. Email notifications get buried. Slack channels fill up. Critical alerts compete with routine informational messages for the same limited human attention.

The core challenge is calibration: teams need to see important issues immediately, without drowning in noise. Too few alerts means missed incidents. Too many means alert fatigue, where engineers start ignoring notifications entirely — including the ones that matter.

On paper, this looks like an ideal problem for artificial intelligence to solve. AI could, in theory, filter and prioritize the incoming stream of alerts, surfacing only what requires immediate human attention. But the practical reality is more nuanced.

A Reality Check on AI Capabilities

The marketing narrative around AI often implies a near-sentient system that autonomously detects problems before they occur. In practice, most AI approaches used in IT operations (sometimes grouped under the term AIOps) rely on pattern recognition and anomaly detection trained on historical data — not generalized intelligence.

This dependency on historical data is a meaningful constraint for DevOps teams. Consider these common scenarios:

• New systems or features: When a team deploys entirely new infrastructure or launches a new application, there is no historical baseline for the AI model to learn from. It cannot know what “normal” looks like yet.
• One-off incidents: Many significant production incidents are novel. They don’t look like anything that happened before, which is precisely why they’re hard to catch.
• Project-specific metrics: Alert patterns from one DevOps project often don’t generalize cleanly to another. Different services, different architectures, and different SLAs produce different signal characteristics.

Acknowledging these limitations doesn’t mean AI has no place in alert management — it means teams should be deliberate about where and how they apply it.

Where AI Can Add Real Value Today

As more enterprise workloads migrate to cloud services, the volume of automated alerts has grown substantially. Managed cloud providers and SaaS vendors typically default to conservative notification settings — alerting on almost everything to avoid missing a critical event. The result is that subscribing IT departments are often overwhelmed with notifications, most of which require no action.

This high-volume, multi-source alert environment is where AI can make an immediate difference, even without perfect historical data. The practical opportunity is alert aggregation and triage.

Aggregating Alerts into a Central Inbox

The first step is consolidating alerts from multiple services and vendors into a single centralized stream. This is not an AI problem — it’s a data pipeline problem. Tools like PagerDuty, OpsGenie, or a custom Kafka-based ingestion layer can route alerts from AWS CloudWatch, Datadog, Prometheus, third-party SaaS vendors, and internal systems into one place.

Once alerts are centralized, AI models have a corpus to work with. Importantly, teams can bootstrap this process by manually labeling historical alerts with severity or action classifications. This labeled dataset — even if imperfect — gives a machine learning model something to train on.

Training a Triage Model

With hand-labeled historical alerts, a classification model can be trained to recognize patterns associated with different severity levels. The model might look at:

• Keywords and phrases in alert titles and message bodies
• Error codes or status codes
• Time of day or day of week (distinguishing scheduled maintenance windows from unexpected events)
• Source service or system
• Alert frequency and recurrence patterns

The trained model then acts as a secondary layer that classifies and routes each incoming alert. For example:

• Critical alerts are relayed immediately to the on-call engineer.
• Moderate alerts are held and only escalated if the condition persists or repeats within a defined window.
• Low-severity or informational alerts — such as scheduled maintenance notifications — are batched into a daily digest.

This approach doesn’t require AI to understand the underlying systems in depth. It requires only that the model can reliably distinguish high-signal from low-signal messages based on the features available in the alert text and metadata.

Multi-Channel Alert Routing

An AI-assisted alert management system can also handle routing logic — deciding not just when to notify, but how and to whom. Different team members may have different preferences and roles:

• On-call engineers might receive immediate SMS or phone alerts for P1 incidents.
• Team leads might receive the same notification via Slack or Microsoft Teams.
• Stakeholders outside the engineering team might receive a summary email.

While much of this routing logic can be hardcoded into rule-based systems, an AI model has one meaningful advantage: it can update its classification behavior as new data arrives, without requiring manual rule changes. Adding a new cloud service or onboarding a new vendor introduces new alert formats. A self-updating model can adapt over time with less ongoing maintenance than a static rule engine.

Honest Expectations: What AI Won’t Do

Even a well-implemented AI alert management system will make mistakes. It will occasionally suppress an alert that turns out to be important, or escalate noise as a critical issue. These errors are especially likely early in the model’s lifecycle, before sufficient labeled data has accumulated.

Teams adopting AI for alert management should plan for:

• A human review loop: Engineers should periodically audit suppressed alerts to catch misclassifications and provide corrective feedback to the model.
• Graceful fallback: If the AI system fails or produces low-confidence classifications, alerts should default to delivery rather than suppression.
• Transparency: The system should make it easy to see why an alert was classified a certain way, so engineers can trust — and correct — its decisions.

Summary

AI is not a silver bullet for DevOps alert fatigue, but it is a practical tool for triage and routing when applied honestly. The most realistic near-term application is training a classification model on labeled historical alerts to create a secondary filtering layer — one that handles the volume problem so engineers can focus on genuine incidents.

The key prerequisites are: centralizing alerts from all sources, investing time in labeling historical data, and building in the human oversight needed to correct the model over time. Teams that approach AI alert management with those foundations in place are likely to see meaningful reductions in alert fatigue — without the risk of over-relying on a system that isn’t yet ready to operate fully autonomously.

Why DevOps Transformations Fall Short

More organizations than ever are positioning software as a core competitive differentiator. That shift raises the stakes: customers expect faster releases, more reliable services, and quicker responses to bugs and security issues. DevOps emerged as a discipline designed to meet those expectations by aligning people, processes, and tooling around rapid, repeatable, high-quality delivery.

Yet the gap between adopting DevOps and benefiting from it is wider than most teams anticipate. Culture, structure, and leadership all play a role—and missteps in any of these areas can stall or reverse progress. Understanding where organizations typically go wrong is the first step toward getting it right.

Mistake 1: Misunderstanding Goals and Definitions

One of the most pervasive mistakes is treating DevOps as a label rather than a practice. Renaming an existing IT team “DevOps” or deploying a CI/CD tool without changing underlying workflows does not constitute a DevOps transformation. Without a shared, organization-specific definition of what DevOps means—and what success looks like—teams have no reliable way to measure progress or identify failure.

Before committing resources, answer these foundational questions:

• What problem are we solving? Reduce deployment frequency gaps? Shorten mean time to recovery (MTTR)? Improve release reliability?
• How will we measure success? DevOps lends itself to concrete metrics: deployment frequency, lead time for changes, change failure rate, and MTTR are the four key metrics popularized by the DORA research program.
• What does “done” look like for Phase 1? A staged roadmap with measurable milestones prevents scope creep and keeps teams motivated.

Defining measurable goals is not bureaucracy—it is what transforms DevOps from a buzzword into an engineering discipline. Watching deployment lead time drop from days to hours is a concrete, motivating signal that the transformation is working.

Mistake 2: Fear of Change

Overly cautious organizations often undermine their own DevOps investments. Excessive manual approval gates, reluctance to automate testing, and resistance to shifting responsibility left toward developers all create friction that negates the speed advantages DevOps is designed to deliver.

This fear is understandable. Faster change can feel riskier, and legacy systems may not be designed for continuous deployment. But the antidote is not to slow down—it is to invest in the safety nets that make speed safe:

• Automated testing pipelines that catch regressions before code reaches production.
• Feature flags that decouple deployment from release, allowing gradual rollouts.
• Automated rollback mechanisms that reduce the blast radius of a bad deployment.
• Blameless post-mortems that treat failures as learning opportunities rather than disciplinary events.

The cultural dimension is equally important. DevOps requires teams to fundamentally rethink how they view software delivery—from a project hand-off model to a continuous, shared responsibility model. That shift can be disorienting, and change management support from leadership is essential to ease the transition rather than force it.

Mistake 3: Overestimating What DevOps Alone Can Do

DevOps is not a self-executing strategy. Deploying a Kubernetes cluster, a GitOps workflow, or a suite of observability tools will not automatically unite siloed teams or fix a culture of blame and mistrust. Real, sustained leadership is required to drive the organizational changes that make tooling investments pay off.

Common symptoms of overestimating DevOps include:

• Teams adopt CI/CD pipelines but still deploy infrequently because approvals remain manual and risk-averse.
• Monitoring tools are deployed but no one owns the alerts or acts on the data.
• “DevOps” becomes a siloed team of its own, reinforcing the wall between development and operations rather than removing it.
• Leadership announces a DevOps initiative but does not fund training, restructuring, or dedicated champions.

DevOps requires ongoing advocacy—not just at launch but continuously, as the practice evolves and as teams encounter new challenges. Organizations that appoint DevOps champions embedded within product teams, rather than centralizing responsibility in a separate platform team with no mandate to influence culture, tend to sustain their gains more effectively.

Practical Takeaways for Engineering Leaders

A DevOps transformation is a long-term investment, not a one-time project. Keeping these principles in mind helps organizations stay on course:

1. Define DevOps for your context. Agree on a working definition and measurable objectives before writing any job descriptions or purchasing any tools.
2. Build safety before building speed. Automated testing, observability, and rollback capabilities make it rational—not reckless—to deploy more frequently.
3. Lead the culture, not just the toolchain. Leadership must model the behaviors—transparency, shared ownership, psychological safety—that DevOps requires.
4. Measure and iterate. Use DORA metrics or equivalent indicators to track progress, identify bottlenecks, and make the case for continued investment.
5. Expect and embrace the learning curve. Setbacks are data points. A blameless culture turns failures into improvements rather than sources of fear.

Staying competitive in a software-driven market increasingly depends on the ability to deliver reliably and quickly. The organizations that avoid these common mistakes—vague goals, change aversion, and misplaced faith in tooling alone—are the ones that turn DevOps from a promise into a measurable engineering advantage.

What Was Predicted for DevOps in 2020

As 2019 drew to a close, analysts at Forrester Research laid out their forecasts for where DevOps practices would head in 2020. Three predictions stood out: chaos engineering moving into governance contexts, business value displacing velocity as the primary success metric, and the rise of embedded, just-in-time developer learning tools. Each of these reflected real tensions that engineering teams were already grappling with at the time.

Prediction 1: Chaos Engineering as a Controls Strategy

For most of its early history, chaos engineering was treated as a purely operational discipline — a way to harden distributed systems by deliberately injecting failure. The conventional wisdom was that production was sacred and that risk had to be contained through environment boundaries: development, staging, QA, and only then production.

The Forrester prediction challenged that framing. As systems grow too complex to fully exercise in pre-production environments, chaos engineering becomes one of the few practical tools for identifying risk at scale. Rather than viewing “testing in production” as inherently reckless, the argument was that disciplined chaos experiments — with proper observability and blast-radius controls — could serve as a legitimate mechanism within governance, risk, and compliance (GRC) frameworks.

In practice, this means treating chaos experiments as structured risk identification exercises rather than cowboy operations. Teams that can demonstrate they have systematically probed failure modes in production-like conditions are arguably in a stronger compliance posture than teams that rely entirely on pre-production testing that cannot replicate real-world scale or dependency complexity.

Key implications for engineering teams

• Chaos experiments should be documented, scoped, and tied to specific risk hypotheses.
• Observability infrastructure (metrics, tracing, alerting) must be mature before meaningful chaos testing is possible.
• GRC and platform engineering teams need a shared vocabulary for discussing controlled failure injection as a risk management practice.

Prediction 2: Business Value Will Overtake Velocity as the Favored Metric

Deployment frequency, lead time for changes, and similar velocity-oriented metrics — popularized through frameworks like DORA — were the dominant way DevOps teams demonstrated progress. The prediction was that this would shift in 2020, with business outcomes taking priority over throughput proxies.

The underlying driver is the narrowing gap between IT and the broader business. As digital products become core revenue streams rather than supporting infrastructure, executives and product owners increasingly want to know what software delivery is actually producing in terms of customer value, revenue impact, or risk reduction — not just how fast code moves through a pipeline.

Velocity metrics are valuable, but they are proxies. A team deploying fifty times a day can still be building the wrong things, or shipping features that do not move key business outcomes. The prediction anticipated that mature DevOps organizations would add outcome-oriented metrics — things like feature adoption rates, customer retention impact, or revenue attributable to specific releases — alongside, or instead of, pure throughput measures.

Practical considerations

• Teams need instrumentation that connects deployments to downstream business events, not just pipeline stages.
• Product management and engineering need shared dashboards and a common definition of “value delivered.”
• Velocity metrics remain useful for diagnosing bottlenecks; the shift is about what gets reported upward, not about discarding engineering-level telemetry.

Prediction 3: New Tools Will Help Developers Learn Without Leaving Their Workflow

The third prediction addressed a structural challenge in how engineering teams are built and maintained. The traditional approach — rotating individuals across teams based on skill-set needs — was identified as increasingly counterproductive. Research into high-performing teams consistently shows that cohesion, shared context, and established working relationships are significant contributors to team effectiveness. Constant reorganization erodes these qualities.

The alternative is keeping teams stable while investing in continuous learning within the team. However, the pace of change in development tooling, cloud platforms, and architectural patterns makes traditional training approaches — multi-day offline courses, certification programs, batch learning — poorly suited to the pace of daily engineering work.

The prediction was that tooling would emerge to support “just enough” learning integrated directly into the development workflow. Rather than pulling engineers away from their work to learn new technologies, learning would be embedded in the context of real tasks — documentation surfaces, interactive examples, AI-assisted guidance, or in-IDE prompts that provide relevant knowledge at the moment of need.

What this looks like in practice

• Context-aware documentation and code examples surfaced inside the IDE or CI/CD interface.
• Lightweight learning modules triggered by the specific technology or pattern a developer is currently working with.
• Team-level knowledge sharing practices (internal wikis, architecture decision records, mob programming sessions) that spread knowledge without requiring individuals to leave the team.

Beyond the Three: Broader Trends Worth Watching

The Forrester report also noted two additional dynamics that were shaping the DevOps landscape at the time. First, DevOps practices were expanding into new vertical markets — industries like financial services, healthcare, and government that had historically been slower adopters. Second, and perhaps counterintuitively, release frequency across parts of the industry was stagnating or declining, suggesting that the broad DevOps adoption narrative was masking significant variance in actual practice maturity.

Both of these trends point to a maturing field that is moving beyond its early-adopter phase into the harder work of mainstream adoption — with all the organizational, regulatory, and tooling challenges that entails.

Takeaways for Engineering and Platform Teams

Looking back from a later vantage point, these predictions identified real directional forces even if the specific timelines were compressed or extended by events. Chaos engineering has continued to mature as a discipline. Business outcome measurement remains an active area of investment for platform and product engineering teams. And the question of how to support continuous learning without disrupting team stability has become more pressing, not less, as the tooling landscape has expanded rapidly.

For teams building or refining their DevOps practices today, these three themes — risk-aware experimentation in production, outcome-oriented metrics, and embedded continuous learning — remain worth engaging with directly.

Original analysis by Principal Analyst Charles Betz, Forrester Research. Originally published via Forbes.

To align database operations with modern application delivery, IT managers must rethink how they handle database change management and bring it inside the Continuous Integration and Continuous Deployment (CI/CD) process. This is easier said than done. Many organizations run hybrid database environments that combine on-premises infrastructure with cloud platforms, and mix proprietary and open source database engines — all of which adds complexity to any standardization effort.

Why Database Changes Still Lag Behind Application Delivery

Historically, database change management has operated on long release cycles — often ranging from six weeks to three months — driven by manual SQL script reviews, late-stage DBA involvement, and the inherent risk of applying schema or data changes to a live production environment. Unit testing of procedural database code adds another layer of complexity that most CI/CD toolchains were not originally designed to handle.

The result is a two-speed IT model: application teams shipping changes weekly or even daily, while database changes queue up and become a bottleneck. Organizations that have successfully closed this gap report spending less time on unplanned rework, faster release cycles, lower operational costs, and measurable revenue impact.

Three Benefits of Integrating the Database into CI/CD

1. Reduce Outages and Deployment Errors

In a traditional workflow, DBAs review SQL scripts close to deployment time. Late-stage reviews mean errors are caught at the worst possible moment — when changes are hardest to roll back and outages are most costly. Common failure modes include untested schema migrations, environment mismatches, and manual steps that introduce human error.

Bringing database changes into the CI/CD pipeline — with version control for schema objects, automated build validation, and scripted deployment steps — shifts error detection left. Problems surface during development or testing rather than in production. Key CI/CD enablers for database work include:

• Version control for schema definitions — treating DDL and DML scripts the same way developers treat application code.
• Automated build validation — catching syntax errors and dependency issues on every commit.
• Automated rollback scripts — ensuring every forward migration has a corresponding undo step that has been tested.
• Policy gates in the pipeline — blocking deployments that violate naming conventions, index standards, or security policies before they reach production.

The cumulative effect is fewer unplanned outages and a shorter mean time to recovery when issues do occur.

2. Improve Release Management Through Better Staging Environments

One of the most persistent sources of production surprises is environment drift — a staging database that no longer resembles the production schema, or a test dataset that doesn’t reflect real-world data volumes and distributions. When staging environments diverge from production, test results become unreliable and teams lose confidence in their releases.

A DevOps approach standardizes environment provisioning. Database schemas, configuration settings, and seed data are defined as code and applied consistently across development, test, staging, and production environments using the same pipeline. Benefits include:

• Test results that more accurately predict production behavior.
• Higher-quality production deployments with fewer performance surprises.
• Reduced risk of scalability issues appearing for the first time in production.
• Clearer audit trails showing exactly which schema version is running in each environment at any point in time.

3. Achieve Smaller, More Manageable Iterations

Large, infrequent database releases are inherently risky. Bundling dozens of schema changes into a single deployment makes it difficult to isolate the root cause when something goes wrong, and rollback becomes a high-stakes, all-or-nothing operation.

Integrating database change management into CI/CD encourages smaller batch sizes. DBAs and developers collaborate to break work into incremental migrations — adding a column here, creating an index there — each of which is independently tested and deployed. Smaller changes are:

• Easier to review and approve.
• Faster to test in isolation.
• Simpler to roll back if a problem is detected.
• Less likely to cause cascading failures.

This approach also aligns naturally with feature-flag patterns and expand/contract schema migrations, allowing teams to decouple database deployments from application deployments entirely.

Breaking Down the DBA Silo

In a pre-DevOps model, the production DBA was the final gatekeeper — receiving change scripts from development teams, often with little context, and left to determine whether they were safe to apply. Without visibility into application logic, and under pressure to maintain uptime, DBAs were forced into a conservative, slow-moving posture.

In a DevOps culture, development, operations, and database teams collaborate from the planning stage. Schema changes are discussed alongside feature work, reviewed early, automated through the pipeline, and tracked in the same version control system as application code. The DBA role shifts from reactive gatekeeper to proactive platform engineer — designing migration frameworks, setting automation standards, and enabling the rest of the team to ship database changes safely and independently.

Getting Started: Practical First Steps

For teams that are new to database DevOps, the transition does not have to be all-or-nothing. A phased approach reduces risk:

1. Put all schema objects under version control. If your DDL scripts are not in Git today, start there. This alone improves auditability and reproducibility.
2. Automate environment provisioning. Use migration tools (such as Flyway, Liquibase, or similar) to apply schema changes consistently across environments.
3. Add database steps to the existing CI pipeline. Run migration scripts against an ephemeral test database on every pull request. Fail the build if migrations do not apply cleanly.
4. Establish rollback procedures. Write and test undo scripts for every migration before the migration is merged.
5. Standardize staging environments. Invest in tooling that keeps staging schema in sync with production at all times.

The Bottom Line

The gap between application delivery speed and database change management is one of the most underappreciated bottlenecks in modern software delivery. Organizations that integrate database operations into their DevOps CI/CD pipelines gain three concrete advantages: fewer production outages, more reliable release management, and faster iteration cycles through smaller, safer changes.

The shift requires executive support, cultural change, and investment in the right tooling — but the organizations that have made the transition consistently report better outcomes. Those that continue to treat the database as an external constraint to the DevOps process will find it increasingly difficult to compete with teams that do not.

This article is based on analysis originally published on JAXenter.com (2019).

Background: Puppet’s Mission to Automate the Grind

Puppet was founded in 2005 with a stated purpose: eliminate soul-crushing work from IT. More than a decade later, that mission still shapes the company’s product direction. With tens of thousands of organisations relying on Puppet to manage infrastructure configuration and software delivery, the company has continued to expand its scope—from server configuration management into multi-cloud orchestration, continuous compliance, and cloud-native deployment automation.

In late 2019, Puppet announced two significant developments: Project Nebula, a new platform for cloud-native continuous deployment, and a sharpened focus on continuous compliance, continuous deployment pipelines, and automated incident remediation.

What Is Project Nebula?

Project Nebula is Puppet’s answer to the fragmentation problem in cloud-native delivery pipelines. It provides a common workflow for DevOps teams to build, provision, and deploy cloud-native applications across dev, test, and production environments—with the goal of enabling rapid, low-risk deployments.

The core design principle is familiar to anyone who has used Puppet’s configuration management tools: declarative, reusable workflows that abstract away underlying complexity. Teams define what the end state should look like; Nebula handles the orchestration required to get there.

Why Puppet Built It

During an initial private beta, Puppet identified two recurring pain points that were blocking organisations from achieving true continuous deployment with cloud-native applications:

• Toolchain fragmentation: Teams lacked a single tool capable of automatically composing multiple cloud-native utilities—covering infrastructure provisioning, application deployment, and notifications—into a coherent, end-to-end deployment workflow.
• No shared platform for teams: Individual teams were building deployment workflows in isolation, with no consistent way to share those workflows or propagate best practices across the organisation.

Puppet’s response was to apply the same declarative principles that made configuration management automation tractable to the cloud-native continuous deployment problem. Teams already practising infrastructure as code can bring those same mental models—desired state, idempotency, reusable modules—into their Kubernetes and serverless workflows.

What Nebula Means for IT Teams in Practice

Project Nebula allows DevOps platform teams to create reusable workflow templates that application developers and infrastructure administrators can consume on demand. The intended outcome is a self-service delivery model where developers can spin up environments for dev, test, or production use cases without depending on specialist infrastructure knowledge every time.

For organisations still running traditional workloads alongside cloud-native services, Nebula is designed to sit alongside existing Puppet tooling rather than replace it—providing continuity as teams modernise incrementally.

The Real Challenges of Cloud-Native Adoption at Scale

Puppet’s perspective on cloud-native adoption is notably clear-eyed. While the promise of containers and serverless architectures is that they simplify operations, the company argues the opposite is true in production at scale.

Getting started with cloud-native technologies is relatively straightforward. Scaling those technologies across multiple teams, while maintaining security and compliance, is where organisations consistently run into trouble. The reasons include:

• Steep learning curves: New workflows and toolchains require significant re-skilling, and mistakes are costly in production.
• Misplaced expectations: There is a common misconception that cloud-native architectures eliminate the need for dedicated infrastructure and operations expertise. In reality, responsibilities such as security patching, compliance reporting, credential rotation, and monitoring configuration do not disappear—they simply need to be automated.
• Operational complexity at scale: Multi-cloud and hybrid environments introduce more moving parts, not fewer. Organisations that underinvest in infrastructure engineering early tend to encounter compounding reliability and security problems as workloads grow.

Puppet’s recommended response is to invest deliberately in infrastructure developers—engineers who specialise in building and managing the automation layer that supports everything else—and to treat automation as a first-class requirement rather than a future optimisation.

Continuous Compliance: Moving Beyond Point-in-Time Audits

Compliance is one area where Puppet sees significant room for automation to eliminate manual, repetitive work. The company’s vision for continuous compliance involves embedding policy checks throughout the delivery pipeline rather than treating compliance as an end-of-cycle gate.

A mature continuous compliance pipeline, as described by Puppet, would work as follows:

1. Source code scanning: Static analysis and policy checks at the point of code commit, catching compliance violations before they reach a running environment.
2. Pipeline integration: A continuous verification step integrated into the test and deployment phases, validating infrastructure and application configuration against defined compliance policies (for example, PCI DSS controls for payment card environments).
3. Runtime scanning: Ongoing scanning of running applications and infrastructure to detect drift or new violations.
4. Automated remediation: When a violation is detected in a running environment, the pipeline automatically triggers a new deployment that incorporates the required fixes, returning the environment to a known-good compliant state without manual intervention.

Achieving this level of automation also requires organisational change. Teams need to redesign processes around automated compliance verification and develop sufficient confidence in their policy-as-code definitions to reduce—or in some cases eliminate—manual pre-deployment approval gates.

Puppet’s Broader Modernisation Strategy

Puppet positions infrastructure automation as the foundation on which application modernisation depends. The company’s argument is straightforward: if your infrastructure cannot be placed reliably into a known, desired state on demand, everything built on top of it becomes harder to deliver, secure, and scale.

The company’s strategy for helping organisations modernise has two layers:

• Infrastructure abstraction: Giving teams tools to describe desired infrastructure state declaratively, making applications more portable and infrastructure more auditable—regardless of whether workloads run on bare metal, virtual machines, or cloud-native platforms.
• Multi-cloud continuity: Allowing teams to reuse workflows, content, and automation logic across traditional and cloud-native environments, so organisations are not forced to start from scratch as they migrate workloads or adopt new platforms.

Project Nebula is the most visible expression of this strategy in the cloud-native space, applying concepts that Puppet refined in server configuration management to Kubernetes-based and serverless deployment workflows.

Key Takeaways for DevOps Teams

• Cloud-native adoption does not reduce operational complexity—it shifts and redistributes it. Plan your team structure and tooling accordingly.
• Declarative, infrastructure-as-code principles that work for server configuration can be extended to cloud-native deployment pipelines. Nebula is Puppet’s implementation of that idea.
• Continuous compliance requires embedding policy checks at every stage of the pipeline, not just at release time. Automated remediation closes the loop without manual intervention.
• Automation is no longer optional in multi-cloud environments. Organisations that treat it as a future nice-to-have will encounter security and reliability problems at scale.
• Self-service delivery models depend on reusable, well-documented workflow templates that platform teams build and maintain for the broader organisation.

Original interview source: DevOps Online (devopsonline.co.uk), October 2019.

Overview

Enterprise Strategy Group (ESG), working with Data Theorem — a provider of tools for securing APIs and mobile applications — surveyed 371 IT and cybersecurity professionals at North American organizations to assess the state of DevSecOps adoption. The results paint a picture of genuine momentum tempered by fragmented implementation, organizational silos, and tool sprawl.

Where DevSecOps Adoption Stands

Headline adoption figures look encouraging on the surface:

• 55% of respondents have already incorporated security into their DevOps processes.
• 22% plan to do so.
• 20% are still in the evaluation stage.

However, the survey reveals that adoption is uneven in practice. Only 33% of organizations involve cybersecurity teams at the start of the application development process — a foundational requirement for genuine shift-left security. And just 39% report that cybersecurity professionals are involved in more than half of their cloud-native application projects today, though that figure is projected to reach 78% within two years.

Coverage of actual applications is even more telling: only 8% of respondents are securing 75% or more of their cloud-native applications with DevSecOps practices right now. Within two years, 68% expect to reach that threshold — a dramatic projected shift that underscores both the ambition and the gap.

The Multi-Platform Security Challenge

One reason consistent DevSecOps is difficult to achieve is the diversity of platforms organizations already run. According to the survey, workloads are distributed across:

• Virtual machines: 34%
• Bare-metal servers: 28%
• Containers: 23%
• Serverless computing frameworks: 15%

Only 10% of organizations currently run more than half their workloads on public cloud, but that proportion is forecast to rise to 30% within two years.

This sprawl directly drives the top reported security challenges:

• Consistently managing cybersecurity across multiple platforms: 43%
• Cost and complexity of implementing controls across platforms: 35%
• Lack of understanding of threat vectors facing cloud-native applications: 35%

Container and Serverless Security Gaps

For teams working with containers, the survey identified two equally-cited top concerns:

• Aligning security controls with deployment models: 32%
• Verifying that images in registries meet compliance requirements: 32%

For serverless environments, API vulnerabilities were the most commonly cited concern at 32%. Budget allocations reflected these priorities closely: vulnerability scanning of registry-resident container images (26%) narrowly edged out API security tooling (25%) as the leading spend category.

Tool Sprawl and the Vendor Consolidation Dilemma

One of the more striking findings involves the tension between tool specialization and platform consolidation. The survey found:

• 73% believe their organization uses too many specialized products to secure cloud-native applications properly.
• 50% expect their organization to consolidate controls by procuring from a smaller set of vendors — or even a single vendor.
• Yet 59% still prefer disparate point controls to achieve more functionality, even if it means managing multiple tools across different compute types and locations.

This contradiction is common in security purchasing decisions: teams recognize the overhead of fragmented tooling but are reluctant to trade away functionality for simplicity. The most important product attributes cited by respondents were a rich set of pre-deployment capabilities, runtime security features, support across mixed server workload types, and flexible deployment options.

Organizational Structure: Still a Bottleneck

Perhaps the most fundamental barrier identified in the study is organizational design itself. 82% of respondents said their organization has different teams assigned to secure cloud-native applications. Among that group:

• 50% plan to merge these responsibilities in the future.
• 32% intend to keep them separate.

As ESG Senior Analyst and Group Director Doug Cahill noted, cybersecurity is increasingly being baked into applications rather than bolted on after the fact. But achieving that consistently at scale requires dismantling the team boundaries that still keep security downstream of development in most organizations.

Key Takeaways for DevOps and Security Teams

The ESG findings offer several actionable signals for teams navigating their own DevSecOps journey:

• Shift left early and formally. Involving security at the start of development — not just during testing or deployment — is still uncommon. If only a third of organizations do this today, there is real competitive differentiation available to those who do.
• Address the platform coverage gap. Having a DevSecOps process that applies to some pipelines but not others is a significant risk. Audit which workload types and deployment environments lack consistent security controls.
• Prioritize API security. Whether workloads run in containers or serverless environments, API vulnerabilities consistently surfaced as a top concern. Ensure API scanning is part of your CI/CD pipeline, not a post-release afterthought.
• Challenge your tooling assumptions. If your team recognizes tool sprawl as a problem but keeps acquiring point solutions, define explicit criteria for when a platform investment is more appropriate than another specialized tool.
• Plan the organizational change, not just the technical one. Merging security and development responsibilities requires governance decisions, training, and process redesign — not just new tooling.

Conclusion

The ESG/Data Theorem study confirms what many practitioners already sense: broad awareness of DevSecOps has not yet translated into consistent, organization-wide practice. The two-year projections — 78% cybersecurity team involvement in cloud-native projects, 68% securing 75% or more of applications — reflect strong intent, but intent and execution are very different things. The organizations that close the gap fastest will be those that treat DevSecOps as a structural and cultural change, not merely a tooling upgrade.

What Is Managed Cloud Networking?

Cloud networking describes an approach in which all core networking resources — routing, switching, security, and management — are centralized and delivered over the internet rather than maintained on dedicated on-premises hardware. A managed cloud networking platform extends this further: a third-party provider operates, monitors, and optimizes the entire network fabric on behalf of the enterprise, typically under a service-level agreement (SLA).

Common workloads handled across these platforms include network management, virtual desktop infrastructure (VDI), Voice over IP (VoIP), and unified communications. By shifting network operations to the cloud, organizations can reduce the capital expenditure associated with owning and refreshing physical networking equipment while gaining elastic capacity and centralized visibility.

Key Market Drivers

Several converging forces are accelerating enterprise adoption of managed cloud networking platforms:

• Demand for operational simplicity: Distributed branch offices and remote workforces create complex networking environments. Managed cloud platforms reduce that complexity by centralizing policy, configuration, and monitoring.
• Cost pressure: Moving from capital expenditure (CapEx) network infrastructure to an operational expenditure (OpEx) subscription model appeals to finance and IT leadership alike.
• Adoption of IoT and AI workloads: As organizations deploy Internet of Things sensors and machine-learning pipelines, they require network infrastructure that can scale dynamically and handle unpredictable traffic patterns — a natural fit for cloud-native networking.
• Mobile and application hosting growth: The proliferation of mobile workforce tools and SaaS applications puts pressure on traditional WAN architectures, pushing organizations toward SD-WAN and cloud-managed alternatives.
• Hybrid cloud strategies: Enterprises operating across both private and public clouds need consistent networking policies, driving demand for platforms that can manage connectivity across heterogeneous environments.

Key Market Restraints

Despite strong tailwinds, managed cloud networking adoption faces meaningful headwinds:

• Security and privacy concerns: Routing all organizational traffic through a third-party managed platform raises questions about data sovereignty, confidentiality, and regulatory compliance — particularly in healthcare, financial services, and government sectors.
• Vendor lock-in risk: Migrating network configurations and policies from one managed platform to another is non-trivial, making enterprises cautious about long-term commitments.
• Integration complexity: Organizations with legacy on-premises networking equipment may face significant integration effort before they can fully leverage a managed cloud platform.

Market Segmentation

The managed cloud networking platforms market can be analyzed across several dimensions:

By Service Type

• Business services
• Security services
• Mobility services
• Network services
• Data center services

By Cloud Deployment Model

• Public cloud: Networking infrastructure shared across multiple tenants and operated by a hyperscale or specialist provider.
• Private cloud: Dedicated networking resources, either on-premises or hosted, offering greater control and isolation for regulated industries.

By Enterprise Size

• Small and medium-sized enterprises (SMEs)
• Large enterprises

By End-User Industry

• Telecom and IT-enabled services (ITES)
• Banking, financial services, and insurance (BFSI)
• Healthcare
• Government and education
• Retail and consumer goods
• Manufacturing and automotive
• Media and entertainment

Regional Outlook

North America

North America has historically held a leading position in this market, driven by the concentration of major technology vendors, a mature enterprise IT landscape, and early adoption of cloud-first networking strategies. The region’s strong regulatory environment around data management has also pushed vendors to develop robust compliance tooling within their managed platforms.

Asia Pacific

Asia Pacific represents a high-growth opportunity, particularly among small and medium-sized enterprises that lack the internal IT resources to manage complex network infrastructure. Significant cloud infrastructure investment by global hyperscalers in the region has expanded the available service footprint, making managed cloud networking more accessible to regional businesses.

Europe, Middle East & Africa, and Latin America

European adoption is shaped by strict data protection regulations (including GDPR), which influence both architecture choices and vendor selection. Middle East & Africa and Latin America markets are at earlier stages of adoption but are expected to grow as cloud infrastructure matures in those regions.

Notable Vendors in the Space

The managed cloud networking platforms landscape includes a mix of networking specialists, system integrators, and hyperscale cloud providers. Vendors frequently cited in this market segment (as of the original 2019 analysis) include:

• Cisco Systems
• Huawei Technologies
• IBM (International Business Machines)
• Ericsson
• Fujitsu
• NEC Corporation
• NTT Data Corporation
• Accenture
• Alcatel-Lucent (now Nokia)
• Aerohive Networks (acquired by Extreme Networks in 2019)
• Interoute Communications (acquired by GTT Communications in 2018)
• NaviSite

The competitive landscape has continued to evolve since 2019, with major public cloud providers (AWS, Microsoft Azure, Google Cloud) expanding their managed networking service portfolios and SD-WAN vendors entering the managed services space.

Platform Capabilities to Evaluate

When assessing a managed cloud networking platform, engineering and infrastructure teams should evaluate the following capability areas:

• Elastic compute and hosting: Can the platform scale network resources automatically in response to traffic demand?
• Security services: Does the platform include firewall-as-a-service, intrusion detection, and zero-trust network access (ZTNA)?
• Relational database and storage integration: How tightly does the networking layer integrate with managed database (SQL) and object storage services?
• Observability and analytics: What network telemetry, logging, and AI-driven anomaly detection does the platform provide?
• Multi-cloud and hybrid support: Can the platform manage connectivity across multiple public clouds and on-premises environments from a single control plane?
• Compliance and data residency: Does the platform support regional data residency requirements relevant to your industry?

Summary

Managed cloud networking platforms sit at the intersection of several major enterprise IT trends: cloud migration, distributed workforce enablement, IoT expansion, and operational cost reduction. While security concerns and integration complexity remain real barriers, the market continues to mature as vendors build out more comprehensive, compliance-aware offerings. For DevOps and platform engineering teams, understanding this landscape is increasingly relevant as network configuration becomes code and infrastructure-as-a-service models blur the boundary between application delivery and network management.

In early June 2019, Google Cloud suffered a cascading set of faults that rendered multiple service regions unavailable for a number of hours.

This by itself isn’t totally unprecedented; what made it significant was the way it propagated through the very software that was designed to contain it. Moreover, engineers’ initial attempts to correct the issue were thwarted by the failure of that same software architecture. It was the interconnectedness and interdependencies of Google’s management components that contributed to the outage.

The outage

To understand this situation more fully, the following is a short summary of what happened. A maintenance “event” that normally wouldn’t be such a big deal triggered a reaction in GCP’s network control plane, further exacerbated by a fault in that code enabling it to stop other jobs elsewhere in Google’s infrastructure.

The distributed nature of a cloud platform means that although clusters in one area are designed to be maintained independently of clusters in another, if those management processes leak across regions, the failure spreads like a virus. And because these controllers are responsible for routing traffic throughout their cloud, as more of them turned off, network traffic just became that much more constrained, leading to even more failures. In technical terms:

• Network control-plane jobs were stopped on multiple clusters, across multiple regions at the same time
• Simultaneous packet loss and error rates increased across multiple regions
• As a result, key network routes became unavailable, increasing network latency for certain services
• Tooling to troubleshoot the issue became unusable because tooling traffic competed with service traffic

Once the root cause was identified, remediating the failures required unraveling the management paths to take the right processes offline in the right order and apply the necessary fixes:

• Google engineers first disabled the automation tool responsible for performing maintenance jobs
•  Server cluster management software was updated to no longer accept risky requests which could affect other clusters
• Updates were made to store configurations locally, reducing recovery times by avoiding the latency caused by rebuilding systems automatically
• Network fail-static conditions were extended to allow engineers more time to mitigate errors
• Tooling was improved to communicate status to impacted customers even through network congestion

Ultimately, the fault did not lie in Google’s willingness or ability to address issues, but rather in a systemic problem with how the platform reacted to unforeseen events. In a real-time computing environment, there is no margin during which management systems can be offline to fix a different problem located in the other system used to apply fixes to the first one.

So, what does this teach us about modern IT? It proves the theory that operations need to be centralised across footprints and infrastructure with bias towards platforms over point tools. Here are three specific features you want in a modern IT operations strategy.

A global view for local action

Having a global view of a highly distributed infrastructure is critical. Addressing issues in isolation has the potential of propagating faults into areas not currently under test. You need to create points of observation across all regions simultaneously in order to aggregate management data, thus enabling unified analysis to avoid compounding disparate events. This will also help you build a global service performance model by understanding activity throughout your infrastructure.

You must also consider how management tasks are carried out when systems become unresponsive; protect tooling traffic from service traffic and don’t share the network capacity between operational data and system status / events.

The ability to see impact, impactfully

Your enterprise should design a global impact model. IT operations personnel need to understand topology-driven impact before making changes – even automated changes. Google’s automation did not consider an impact model, and instead was straight policy-driven automation that scaled mistakes automatically. It’s equally important to understand service dependencies via topology mapping, thus enabling impact analysis to take into consideration the cascading effects of policy-driven automation.

Configuration stores you can count on

Lastly, “backing up the backups” by storing configuration data locally instead of relying on a distributed hierarchy can reduce service restoration time. Retrieving this data regionally will increase recovery latency since competing network traffic during a fault will restrict bandwidth available for management tasks.

This outage was essentially a gift to enterprise-class IT operations teams everywhere who think they are prepared for any inevitable service disruptions. It’s taught us all the value in building an IT ops management strategy that includes a singular, global view of dependencies and impacts. Every business thinks they want to become Google. But this is one way you don’t.