What DevOps Automation Actually Means

Amazon Web Services describes the DevOps model as a combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity. Crucially, it also enables development teams to evolve and improve products faster than teams relying on traditional software development and infrastructure management processes.

In practice, DevOps automation spans several layers of the software delivery lifecycle:

• Development: Automated build pipelines, code linting, and static analysis catch issues before they reach review.
• Testing: Automated unit, integration, and end-to-end tests eliminate the manual testing bottleneck that slows releases.
• Infrastructure: Infrastructure-as-Code (IaC) tools allow teams to provision, configure, and tear down environments consistently and repeatably.
• Deployment: Continuous delivery pipelines push validated changes to production — or as close to it as possible — with minimal human intervention.
• Operations: Automated monitoring, alerting, and self-healing systems reduce the operational burden on engineering teams after deployment.

According to Pushpraj Kumar, a business analyst at iFour Technolab, DevOps continuous delivery and consistent cadence of software releases play a major role in the operations strategy of any IT organization. “DevOps accelerates digital services, continuous improvement, and operational flexibility, and could provide innovative and cost-effective ways for delivering high-value automation across development and operations,” he noted.

The Reality: Most Organisations Are Still Getting the Basics Right

There is a temptation to jump straight to discussions of AI-augmented pipelines and autonomous operations. But Michael “Fritz” Fritzius of Arch DevOps offers a useful counterpoint: many companies have not yet mastered conventional DevOps, which makes advanced capabilities like AI or ML even harder to realize.

CTOs, CIOs, and engineering leaders regularly struggle to make sound decisions under deadline pressure. “More often than not, those decisions are rooted in fear, which causes people to guess wrong about the problems needing to be solved, and it creates a mess,” Fritzius said. The consequence is that tooling decisions get made before process and culture are in place — a pattern that undermines the value of any automation investment.

His advice is pragmatic: focus on executing core DevOps well before chasing the next wave of innovation. Automation only amplifies whatever process it is built on top of. If that process is broken, automation makes it worse, faster.

DevOps Is a Culture, Not Just a Toolchain

Rick Stewart, chief software technologist at DLT Solutions, emphasises that DevOps is fundamentally a cultural movement — not a product category. It is built on shared understanding and shared responsibility between developers and operations teams for the software and services they collectively produce.

Organisations that have reached a mature DevOps culture tend to display recognisable characteristics:

• Transparency: Work in progress, failure rates, and deployment frequency are visible to all stakeholders, not just engineering teams.
• Continuous improvement: Teams actively look for inefficiencies in every iteration, not just during post-mortems.
• Automation by default: Manual steps are treated as technical debt to be eliminated, not as the safe or reliable option.
• Infrastructure elasticity: Automated scaling allows resources to expand and contract with demand, converting capital costs into operational costs.

Stewart points out that software delivery is a highly repetitive process, and the removal of manual steps is essential for deploying the most important features frequently and in small increments. Manual activities introduce human error and create bottlenecks — particularly in testing — that compound as teams grow.

“There is a reason why DevOps is expressed as an infinite loop,” Stewart said, referring to the familiar DevOps lifecycle diagram. “Those activities are executed repeatedly, providing feedback to teams and exposing any inefficiencies that can be addressed in a subsequent iteration.”

He also makes an often-overlooked point: a characteristic of a healthy DevOps culture is that it produces joy — not in a trivial sense, but because end users get the features they need on infrastructure that stays up. “By increasing the ability to deliver important features fast on resilient infrastructure, DevOps addresses all stakeholders’ primary needs, which leads to happiness in the workplace.”

Speed, Scale, and the Role of Microservices

Eva Tuczai, then advanced solutions product management and engagement manager at Turbonomic, frames DevOps automation as the intersection of two forces: scale (many independent, loosely-coupled services) and speed (automated pipelines that minimise human overhead at each stage).

The goal is to bring services to market quickly by breaking applications into small, discrete components that can be independently changed, tested, and deployed. How quickly any given service can move through the pipeline depends on how well isolated it is from other services — tight coupling between services is one of the most common constraints on DevOps velocity in practice.

Tuczai identified two emerging specialisations forming within mature DevOps organisations:

• TenantOps: Specialists who help application teams onboard to and best leverage internal platforms and pipelines. They provide operational support to app teams on maintaining performance within the platform and using the DevOps process efficiently.
• ResourceOps: Specialists focused on application performance and end-user experience, ensuring that IT resources meet application requirements while avoiding the excessive overprovisioning that is common when teams prioritise risk avoidance over efficiency.

What Comes Next: AI, Compliance, and Value Stream Management

Richard Hawes, director of product marketing for DevOps at ServiceNow, argues that automation, AI, and ML will enable DevOps to scale in large enterprises in ways that were previously impractical — accelerating digital transformation for both those organisations and the customers they serve.

However, he notes a meaningful friction point: even where individual teams become agile and can iterate rapidly, the pace of innovation slows when those teams must participate in the broader business processes that surround software delivery. This friction is most acute in large, regulated organisations, where risk aversion and compliance requirements add gates that were never designed with DevOps velocity in mind.

The areas he sees as most ripe for automation in the near term include:

• Production release gates: Automating the change approval activities traditionally associated with ITIL-style change management, so compliance checks become part of the pipeline rather than a manual step that halts it.
• Continuous compliance: Integrating corporate risk management requirements — security scanning, licence checks, audit evidence collection — directly into the CI/CD pipeline so that compliance is verified continuously rather than audited after the fact.
• Cross-tool management: As organisations standardise on DevOps platforms, Value Stream Management tooling is emerging to provide visibility and governance across the diverse toolchains that exist within even a single large organisation.

“DevOps brings the business closer to the technologists delivering the solutions by focusing work into teams that own a business product or are directly aligned with a value stream,” Hawes said. That alignment — between business outcomes and engineering work — is ultimately what DevOps automation is in service of.

Key Takeaways

• DevOps automation covers the full delivery lifecycle: build, test, infrastructure, deployment, and operations.
• Culture and process must come before tooling. Automation amplifies whatever process it sits on top of.
• Loose coupling between services is a prerequisite for high DevOps velocity — monolithic architectures constrain pipeline speed regardless of tooling.
• Emerging specialisations (TenantOps, ResourceOps) reflect the maturation of DevOps practice in larger organisations.
• The next frontier is automating compliance, release governance, and cross-team coordination — not just code deployment.
• AI and ML hold genuine promise for DevOps, but most organisations will need to consolidate their core automation practices first.

Overview: Datadog, Inc. (NASDAQ: DDOG) in Early 2020

Datadog, Inc. (NASDAQ: DDOG) — the cloud-native monitoring and observability platform — was already drawing significant attention from both the DevOps community and Wall Street by early 2020, having gone public in September 2019. At the time of this snapshot (February 2020), DDOG shares were trading around $45.68, down approximately 0.67% from the prior session’s close of $45.99.

Trading volume had reached roughly 2.69 million contracts in that session, noted as higher than the stock’s average daily volume — a signal of elevated investor interest. The float stood at approximately 25.94 million shares, and the stock had experienced a -5.81% move over the trailing seven-day period.

Price Range and Analyst Outlook

At the time, DDOG’s 52-week trading range was between $27.55 (low) and $50.12 (high). At approximately $45.68, the stock was:

• Trading roughly 8.86% below its 52-week high
• Trading approximately 65.81% above its 52-week low

This context matters for DevOps professionals and technical investors alike: Datadog had only recently listed its shares, and price discovery was still actively underway. Analyst sentiment at the time was described as mixed, with a range of 12-month price targets reflecting uncertainty about how quickly the company could translate rapid revenue growth into sustainable profitability.

Fundamental Analysis: Profitability Metrics

For those assessing DDOG beyond its product reputation, the early 2020 profitability picture was a mixed one — common for high-growth SaaS companies reinvesting aggressively in sales, engineering, and infrastructure.

The gross margin of 75.48% is the standout figure here. For a cloud infrastructure and observability platform, a gross margin at that level signals strong unit economics — the underlying software business is efficient even if total company profitability is negative. This is typical of high-growth SaaS companies that deliberately run operating losses to capture market share.

The negative operating and net margins reflected heavy investment in R&D and sales and marketing, not fundamental business weakness. Investors familiar with similar-stage SaaS companies (such as early-stage Splunk, New Relic, or Elastic) would recognise this pattern.

Capital Structure and Debt Ratios

Datadog’s debt load at this point was modest relative to its capital base:

• Total Debt to Total Equity: 7.72
• Total Debt to Total Capital: 7.17
• Total Debt to Total Assets: 5.82
• Total Debt to Enterprise Value: 0.01

The near-zero Total Debt to Enterprise Value ratio (0.01) indicates that Datadog’s market valuation was overwhelmingly driven by equity — not leverage. This is typical of newly public, cash-funded technology companies.

Valuation Metrics

Valuation multiples for DDOG in early 2020 reflected the premium the market placed on its growth trajectory:

• Enterprise Value to Sales: 36.34 — high by traditional standards, but not unusual for SaaS companies growing revenue at over 60% year-over-year
• Enterprise Value to EBITDA: -5,562.00 — negative EBITDA makes this ratio effectively meaningless as a valuation tool; investors were using revenue multiples instead
• Price to Cash Flow Ratio: 218.06
• Current Ratio: 4.47 — indicating strong short-term liquidity
• Receivables Turnover: 4.57
• Total Asset Turnover: 0.60

The Current Ratio of 4.47 is particularly noteworthy: it means that for every dollar of short-term liability, Datadog had $4.47 in current assets. This suggested the company had a comfortable liquidity buffer — important for a company still burning cash to fund growth.

Market Capitalisation and Share Count

At the time of this snapshot, Datadog had approximately 305.08 million shares outstanding, giving it a total market capitalisation of approximately $13.94 billion. For a company that had been public for fewer than six months, this reflected substantial investor confidence in the long-term opportunity in cloud observability and monitoring.

Technical Indicator: Relative Strength Index (RSI)

The stock’s Relative Strength Index (RSI) at the time stood at 52.61. RSI is a momentum oscillator that ranges from 0 to 100:

• RSI above 70 typically signals an overbought condition
• RSI below 30 typically signals an oversold condition
• RSI near 50 suggests the stock is in a neutral, balanced momentum state

At 52.61, DDOG was sitting in a neutral zone — neither extended to the upside nor under significant selling pressure at the time of this reading.

Context: Why Datadog’s Fundamentals Were Always About the Growth Story

For DevOps engineers and technical architects evaluating Datadog as a product, many of the company’s financial metrics in 2020 can appear alarming at first glance — negative margins, high EV/Sales multiples, and negative EBITDA. However, understanding the context is important:

• Gross margins above 70% are a hallmark of high-quality SaaS businesses and indicate that the core product economics are sound.
• Operating losses in this phase are largely a deliberate investment in growth — hiring engineers, expanding integrations, and building out sales capacity to capture market share in a rapidly growing cloud-native monitoring market.
• High revenue multiples (EV/Sales of 36x) reflect market expectations of sustained high revenue growth, not an assumption that the business is currently profitable.

Investors and analysts in early 2020 were not primarily evaluating Datadog on near-term profitability; they were assessing the size of the addressable market, the durability of customer expansion, and the company’s competitive position against alternatives such as Splunk, New Relic, Elastic, and AWS’s native monitoring tooling.

Summary: Key Takeaways from the February 2020 Snapshot

• DDOG was trading near the middle of its then-52-week range, with neutral RSI momentum.
• The company was not profitable at the operating or net level, but carried strong gross margins exceeding 75%.
• Liquidity was healthy (Current Ratio: 4.47), and debt was minimal relative to enterprise value.
• Valuation multiples were elevated, consistent with high-growth SaaS peers at a similar stage.
• The stock had approximately 305 million shares outstanding and a market cap of roughly $13.94 billion at the time.

This article is based on a historical market data snapshot from February 2020 and is intended for educational and informational purposes only. It does not constitute financial or investment advice. Always consult qualified financial professionals before making investment decisions.

Why Kubernetes Operators Matter

One of Kubernetes’ core promises is the automation of operational toil — the repetitive, error-prone work that comes with running modern applications at scale. Kubernetes Operators are the mechanism through which that promise is fulfilled. Without them, teams running complex containerized workloads are leaving significant automation potential on the table.

What Is a Kubernetes Operator?

An Operator is a Kubernetes-native application that extends the Kubernetes API to manage custom resources. In practice, this means encoding the operational knowledge of a human expert — runbooks, procedures, and decision logic — into software that runs continuously inside your cluster.

“Operators are clients of the Kubernetes API that control custom resources. This capability enables automation of tasks like deployments, backups, and upgrades by watching events without editing Kubernetes code.”

— Matthew Dresden, Director of DevOps, Nexient

The key differentiator of the Operator pattern — as opposed to a simple Helm chart or a static manifest — is active, ongoing management. An Operator watches the current state of a resource, compares it to the desired state, and takes action to reconcile the two. That includes handling failover, triggering backups, orchestrating rolling upgrades, and autoscaling — behaviors that would otherwise require a human operator on call.

A Concrete Example

Consider a distributed database cluster running on Kubernetes. If the cluster loses a node, a properly written Operator can automatically detect the event through the Kubernetes watch API, replicate data from a healthy node, and restore the cluster to quorum — all without human intervention.

Dresden frames the stakes clearly: imagine managing a highly available, fault-tolerant, multi-region database using StatefulSets, while simultaneously running several hundred microservices that scale dynamically. Coordinating that manually is not realistic for most teams. Operators make it manageable.

“Operators make large-scale Kubernetes deployments practical by automating difficult, error-prone work that humans — the original ‘operators’ — would otherwise need to carry out.”

— Matthew Dresden, Nexient

4 Facts IT Leaders Should Know About Kubernetes Operators

1. Operators Are Growing Rapidly in Quantity and Adoption

CoreOS first introduced the Operator pattern in 2016. Adoption accelerated significantly with the launch of the Operator Framework in March 2018. Red Hat acquired CoreOS in January 2018 and has since integrated Operator support deeply into the OpenShift platform.

By the time this article was originally published (early 2020), growth was already notable at the ecosystem level. Liz Rice, then VP of Open Source Engineering at Aqua Security and chair of the CNCF Technical Oversight Committee, observed an “explosion” in the number of Operators being built by project maintainers and vendors — making it easier to deploy popular open-source projects inside a Kubernetes cluster.

This proliferation led to the need for discoverability tooling. OperatorHub.io was launched as a community hub where teams can find, share, and install Operators. It was launched by Red Hat in collaboration with Amazon, Microsoft, and Google.

2. Operators Require Ongoing Attention — They Are Not “Set and Forget”

A common misconception is that deploying an Operator transfers all operational responsibility to software. It does not. Operators themselves are software, and like all software they need to be patched, upgraded, and monitored.

“Kubernetes Operators can and will get into weird states that require hands-on attention. Don’t come in thinking that using Kubernetes Operators is a ‘set-it-and-forget’ type of situation.”

— Ben Bromhead, CTO, Instaclustr

Bromhead recommends establishing a clear ownership and escalation workflow for Operators before they reach production. Developer teams managing Operators in-house must track upstream changes, plan for upgrades, and have a procedure for manual intervention when the Operator enters an unexpected state.

Dresden reinforces this: an Operator is not a one-time artifact. “Continuous improvement is a must,” he says. Automating complexity does not eliminate it — it moves it. Problems will still surface, often at the worst possible moment, and the pace of automated change can make it impossible for a human to manually track state across the system.

3. Telemetry and Observability Are Non-Negotiable

Because Operators act autonomously and can change system state at high frequency, comprehensive observability is essential. Without it, the first indication of a problem is often a customer complaint rather than a proactive alert.

Dresden stresses that both the providers of a managed service (the team that wrote the Operator) and the consumers (the teams running workloads under it) need visibility into what the Operator is doing at any given moment. Metrics, logs, and events emitted by the Operator should be treated as first-class telemetry signals — routed into your existing monitoring stack and covered by alerting rules.

“Operators can generate a huge boost in productivity, but only if both the providers and consumers of the managed service have visibility into what the Operators are doing.”

— Matthew Dresden, Nexient

4. Write Declarative Operators, Not Imperative Ones

Dresden flags a common beginner mistake: writing Operators imperatively — that is, encoding a sequence of steps to execute — rather than declaratively — encoding the desired state and letting the reconciliation loop figure out how to get there.

Declarative Operators align with the Kubernetes control-plane model and are significantly more robust. An imperative Operator that issues a specific sequence of API calls can fail in unpredictable ways if any step is interrupted or if the cluster is in an unexpected state. A declarative Operator that continuously reconciles actual state toward desired state handles partial failures gracefully and is much easier to reason about and test.

Writing a proper declarative Operator takes more upfront design work, but the long-term reliability and maintainability gains are well worth the investment — especially in production environments where reliability directly affects customer experience.

Key Takeaways for DevOps and Platform Teams

• Operators encode operational knowledge as code. They watch cluster state and take autonomous action to maintain desired state — handling backups, failover, scaling, and upgrades.
• The Operator ecosystem is large and growing. Check OperatorHub.io before building a custom Operator — a community-maintained one may already exist for your use case.
• Treat your Operators as production software. They need patch management, upgrade planning, and an escalation path for when they enter unexpected states.
• Invest in observability from day one. Instrument your Operators to emit useful metrics, logs, and events that integrate with your monitoring stack.
• Design declaratively. A reconciliation-loop-based approach is more resilient than imperative step-by-step automation and aligns with how Kubernetes itself works.

Kubernetes Operators are not a silver bullet, but for teams running stateful, complex workloads at scale, they represent one of the most powerful tools available for reducing operational toil and achieving genuine, self-managing automation.

What is Ansible? Ansible is an open-source IT automation engine, which can remove drudgery from your work life, and will also dramatically improve the scalability, consistency, and reliability of your IT environment. We’ll start to explore how to automate repetitive system administration tasks using Ansible, and if you want to learn more, you can go much deeper into how to use Ansible with Cloud Academy’s new Introduction to Ansible learning path.

What is Ansible and what can it automate?
You can use Ansible to automate three types of tasks:

Provisioning: Set up the various servers you need in your infrastructure.
Configuration management: Change the configuration of an application, OS, or device; start and stop services; install or update applications; implement a security policy; or perform a wide variety of other configuration tasks.
Application deployment: Make DevOps easier by automating the deployment of internally developed applications to your production systems.
Ansible can automate IT environments whether they are hosted on traditional bare metal servers, virtualization platforms, or in the cloud. It can also automate the configuration of a wide range of systems and devices such as databases, storage devices, networks, firewalls, and many others.

The best part is that you don’t even need to know the commands used to accomplish a particular task. You just need to specify what state you want the system to be in and Ansible will take care of it. For example, to ensure that your web servers are running the latest version of Apache, you could use a playbook similar to the following and Ansible would handle the details.

—
– hosts: webservers
vars:
http_port: 80
max_clients: 200
remote_user: root
tasks:
– name: ensure apache is at the latest version
yum: name=httpd state=latest
– name: write the apache config file
template: src=/srv/httpd.j2 dest=/etc/httpd.conf
notify:
– restart apache
– name: ensure apache is running (and enable it at boot)
service: name=httpd state=started enabled=yes
handlers:
– name: restart apache
service: name=httpd state=restarted
The line in the above playbook that actually installs or updates Apache is “yum: name=httpd state=latest”. You just specify the name of the software package (httpd) and the desired state (latest) and Ansible does the rest. The other tasks in the playbook update the Apache config file, restart Apache, and enablea Apache to run at boot time. Take a read at one of our previous blog posts on how to build Ansible playbooks.

Why Ansible?
There are many other IT automation tools available, including more mature ones like Puppet and Chef, so why would you choose Ansible? The main reason is simplicity. Michael DeHaan, the creator of Ansible, already had a lot of experience with other configuration management tools when he decided to develop a new one. He said that he wanted “a tool that you could not use for six months, come back to, and still remember.”

DeHaan accomplished this by using YAML, a simple configuration language. Puppet and Chef, on the other hand, use Ruby, which is more difficult to learn. This makes Ansible especially appealing to system administrators.

DeHaan also simplified Ansible deployment by making it agentless. That is, instead of having to install an agent on every system you want to manage (as you have to do with Puppet and Chef), Ansible just requires that systems have Python (on Linux servers) or PowerShell (on Windows servers) and SSH.

What is Ansible? A New Learning Path
Although Ansible is easier to learn than many of the other IT automation engines, you still need to learn a lot before you can start using it. To help you with this, Cloud Academy has released its Introduction to Ansible learning path.

This learning path includes three video courses:

What is Configuration Management?: A high-level overview of configuration management concepts and software options.
Getting Started With Ansible: Covers everything from Ansible components to writing and debugging playbooks in YAML.
Introduction to Managing Ansible Infrastructure: An overview of Ansible Tower (Red Hat’s proprietary management add-on to Ansible) and Ansible Galaxy (a place to find and share Ansible content).
Hands-on practice is critical when learning a new technology, so we have included two labs in the learning path:

Getting Started With Ansible: Create your first playbook to build a LAMP (Linux, Apache, MySQL, Python) stack on an EC2 instance.
Ansible: Deploying Web Applications: Learn about roles, templates, and handlers by deploying a Python application.
Finally, you can test your knowledge of Ansible by taking the quizzes.

Watch this short video, taken from the Getting Started with Ansible Course, where we take a look at the most common Ansible use cases.

Conclusion
Whether you need to make your life easier by automating your administration tasks or you’re interested in becoming a DevOps professional, Ansible is a good place to start. Learn how to streamline your IT operations with Introduction to Ansible.

DevOps is becoming recognized as a vital pillar of digital transformation. Because of this, CIOs are becoming enthusiastic regarding how DevOps and open source can completely transform the enterprise culture.

All organizations want to succeed and reach their development goals across all projects. However, in reality, the entire journey is not at all easy as it seems, and it often requires collective efforts and time. In this entire journey, there are some common failures which teams are likely to come across.

In this post, we’ll discuss DevOps mistakes which everyone should know and must avoid. Before that, it is necessary to understand the importance of DevOps in today’s world.

Importance of DevOps in today’s world
DevOps often describes a culture and a set of processes which brings development and operations together to complete software development. It enables organizations not just to create but also improve products at a faster pace than they can with some approaches to software development. DevOps adoption rate is increasing by each passing day. According to Statista many business organizations are shifting towards the DevOps culture and there is an increase of 17% in 2018 from the previous year.

DevOps culture is instrumental in today’s world. The following points briefly highlight the need for DevOps in this era.

It reduces costs and other IT stuff.
Results in greater competencies.
It provides better communication and cooperation opportunities.
The development cycle is fast and innovative.
Deployment failures are reduced to a great extent.
Eight DevOps Mistakes
Many people still don’t fully understand what DevOps means. Without prior knowledge and understanding, many DevOps initiatives fail to get off the ground successfully. Following is a brief description of DevOps’ mistakes, and how they can be avoided to start a successful DevOps journey.

1. Rigid DevOps Process
Compliance with core DevOps tenets is vital for DevOps success; organizations have to make adjustments in active response to meet organization demands. Enterprises have to make sure that while the main DevOps pillars remain stable while implementing DevOps; they make the internal adjustments needed in internal benchmarking of the expected consequences.

Instrumenting codebases in a gritty manner and making them more and more partitioned results in more flexibility and provide DevOps team the ultimate power to backtrack and recognize the root cause of diversion in the event of failed outcomes. But, all adjustments have to be made while staying within the boundaries defined by DevOps.

2. Oversimplification of the process
Indeed DevOps is a complex process. To implement DevOps, enterprises often go on a DevOps Engineer hiring spree or at times, create a new and isolated one. The DevOps department is then responsible for managing the DevOps framework and strategy, and it needlessly adds new processes that are often lengthy and complicated.

Instead of creating an isolated DevOps department, organizations should focus on optimizing their processes to make operational products that leverage the right set of resources. For successful implementation of DevOps, organizations must be capable enough to manage the DevOps framework, leverage functional experts, and other resources that can manage DevOps related tasks like budgeting goals, resource management, and process tracking.

DevOps requires a cultural overhaul. Organizations must consider a phased and measured transition to DevOps implementation by educating and training employees on these new processes. Also, they should have the right frameworks to enable careful collaboration.

3. Not preparing for a cultural change
When you have the right tools for DevOps practices, you likely might come across a new challenge. The challenge will be trying to make your teams use the tools for fast development, continuous delivery, automated testing, and monitoring. Is your DevOps culture ready for all such things?

For example, agile methodologies usually mandate that you ship new code once a week, or once a day. It results in the failure of agile methods. You might also face the same conceptual issues with DevOps. It can be like pulling on a smooth road with a car with no fuel in it.

To prevent this situation, plan for a transition period. Leave enough time for the development and operational team to get used to new practices. Also, make sure that they have a chance to gain experience with the new processes and tools. Ensure that before adopting DevOps, you’ve got a matured Dev and Ops culture.

4. Creating a single DevOps team
The most common mistake which most organizations and enterprises make is to create a brand-new team and task them with addressing all the burdens of a DevOps initiative. It is challenging and complicated for both development and operations to deal with a new group that coordinates with everyone.

DevOps started with the idea of enhancing collaborations between teams involved in the development of software like security, DBMS, and QA. However, it is not only about development and operations. If you create a new side to address DevOps, you’re making things more complicated.

The secret ingredient here is simplicity. Focus on culture by encouraging a mindset of automation, quality, and stability. For instance, you might involve everyone in a conversation regarding your architecture, or about some problems found in production environments in which all the relevant players need to be well aware of how their work influences others.

“DevOps is not about a single dedicated team but about organizations that progress together as a DevOps team.”

5. Not including the security team
DevOps is about more than merely putting the development and operations teams together. It is a continuous process of automation and software development, including audit, compliance, and security. Many organizations make the mistake of not following their security practices in advance.

According to a CA Technologies survey, security concerns were the number-one obstacle to DevOps as cited by 38% of the respondents. Similarly, the Puppet survey found that high-performing DevOps teams spend 50% less time remediating security issues than low performers. These high performing teams found different ways to communicate their security objectives and to establish security in the early phases of their development process.

All DevOps practitioners should evaluate the controls, recognize the risks, and understand the processes. In the end, security is always an integral part of DevOps practices, such as DevSecOps (a practice in which development and operations is integrated with security). For example, if you have some security issues in production, you can address them within your DevOps pipeline through the tools which the security team already uses. DevOps and security practices should be followed strictly, and there should be no compromises. Moreover, other measures should be adopted to avoid cyber-criminals invading the DevOps culture. Invest in cybersecurity markets has become a necessity to avoid situations where attacker can carry out attacks like that of spear phishing and phishing. It is found that out of all attacks on various organizations, 95% of them were a result of spear phishing.

6. Incorrect use of incident management
DevOps teams must have a robust incident management process in place. The incident management needs to be utterly proactive and an ongoing process. It means that having a documented incident management process is imperative to define the incident responses.

For example, a total downtime event will have a different response workflow in comparison to a minor latency problem. The failure to not do so can often lead to missed timelines and preventable projects delay.

7. Not utilizing purposeful automation
DevOps needs organizations to adopt and implement purposeful automation. For DevOps, it is essential to take automation across the complete development lifecycle. It includes continuous delivery, continuous integration, and deployment for velocity and quality outcomes.

Purposeful end-to-end automation is a crucial successful DevOps implementation. Therefore, organizations should look at the complete automation of the CI and CD pipeline. However, at the same time, organizations need to identify various opportunities for automation across functions and processes. This helps to reduce the need for manual handoffs for complicated integrations that need new management in multiple format deployments.

Editor’s Note: Do you use or plan to use Azure for DevOps?
If you want to know all about Azure DevOps services, we recommend our latest cookbook, ‘Azure DevOps Server 2019 Cookbook – Second Edition’ written by Tarun Arora and Utkarsh Shigihalli. The recipes in this book will help you achieve skills you need to break down the invisible silos between your software development teams and transform them into a modern cross-functional software development team.

8. Wrong-way to measure project success
DevOps promises for faster delivery. But, if that acceleration comes at the cost of quality, then the DevOps program is a failure. Enterprises looking at deploying DevOps should use the right metrics to understand project growth and success.

For this reason, it is imperative to consider metrics that align velocity with success. Do focus on the right parameters as it is essential to drive intelligent automation decisions.

Conclusion
Now organizations are rapidly running towards DevOps to stand with competition and become successful but they often make big mistakes. There are mistakes that people commit while implementing a DevOps culture. However, all these mistakes are avoidable, and hopefully, the points mentioned above have successfully cleared your vision to a great extent.

After you overcome all the mistakes and adopt DevOps practices, your organization will surely enjoy improved client satisfaction, and employee morale increased productivity, and agility- all of which helps in growing your business.

The QA Problem in a Microservices World

Modern software is rarely a single, self-contained application. It is a constellation of cooperating services, each with its own lifecycle, deployment cadence, and failure modes. Teams must now contend with distributed timing issues, cache invalidation, data consistency across service boundaries, and horizontal scaling — challenges that simply did not exist in a monolithic world.

Yet despite this fundamental shift in how software is built, the role of the traditional Quality Assurance team has been slow to change. QA has historically meant sitting in front of a user interface — or pointing Selenium at it — and systematically exercising input combinations to trigger error conditions. That approach tests only the visible tip of the iceberg, while the underlying distributed infrastructure remains largely unexamined.

As the Guardian’s engineering team put it: the iceberg has not just grown — it has melted, exists concurrently in multiple locations, and is also on fire.

Rethinking Shared Responsibility for Quality

One response to this problem is to treat quality as everyone’s concern rather than a specialised team’s gatekeeper function. At the Guardian, each product team is expected to take responsibility for supporting its own platform. Rather than maintaining separate, dedicated DevOps or Site Reliability Engineering (SRE) teams, they distribute that responsibility across product teams — a culture of shared ownership.

This philosophy forces the QA function to evolve. If quality is pervasive in everything the organisation does, then the people responsible for quality must be equally pervasive and technically versatile.

Introducing the Software Engineer (Tools and Infrastructure)

The Guardian’s answer to this challenge is the Software Engineer (Tools and Infrastructure) — internally abbreviated as SETI. The title is borrowed from Google’s engineering lexicon, where it described engineers who come from a strong technical background but focus on improving the development process rather than shipping end-user features.

The distinction from a traditional Software Engineer in Test (SET) is important:

• A SET improves quality by testing the finished product — validating the output.
• A SETI improves quality by improving how software is built, deployed, and monitored — working on the process itself.

What a SETI Does

• Designs and maintains Continuous Integration and Continuous Deployment (CI/CD) pipelines
• Builds internal tools that help developers diagnose issues faster
• Creates production monitoring and alerting for live applications
• Carries out performance testing at meaningful scale
• Refactors code to improve maintainability and testability
• Collaborates with product engineers to solve systemic quality problems

What a SETI Does Not Do

• Act as a release gatekeeper or bottleneck
• Stamp software changes with a QA seal of approval
• Write unit tests on behalf of developers all day

At the Guardian, developers peer-review each other’s work and write tests as appropriate. The SETI role exists to improve the conditions and tooling around that work, not to own quality unilaterally.

Embedding SETIs in Product Teams

Each SETI is embedded within an existing product team rather than sitting in a centralised QA department. They are expected to work independently to identify, prioritise, and resolve quality issues — but they spend the majority of their time collaborating directly with other engineers on the team.

Crucially, SETIs also meet regularly across team boundaries to share knowledge: comparing problems they have faced, solutions they have implemented, and identifying opportunities to build reusable tooling or establish shared standards. With enough SETIs distributed across enough teams, the organisation gains a coherent, organisation-wide view of engineering health.

Case Study: Monitoring Features Using Production Data

The Guardian’s mobile team runs large beta programmes to maintain quality in their iOS and Android apps. Despite broad coverage of user-facing changes, some categories of bugs can slip into production and remain undetected for extended periods. One example: a bug that prevented users from seeing dynamic subscription messaging was not caught during internal testing or by beta testers.

To address this class of problem, the SETI team built a monitoring service that allows developers to define automated, periodic checks on how specific features are performing in production. The service is implemented as an AWS Lambda function that queries a Data Lake using the AWS Athena SDK. Although SETIs led the build, they worked alongside product engineers to explain the tooling and encourage adoption for a broader range of features.

Key Takeaway

Production data is a first-class testing signal. Monitoring features continuously against real usage patterns catches entire categories of bugs that pre-release testing cannot.

Case Study: Production-Quality Performance Feedback at Pull Request Stage

When releasing changes to theguardian.com, several categories of automated tests require the full application to be instantiated and run. These include:

• Validating pages against the Google AMP specification
• Server-side load testing
• Browser performance checks, such as Time to Interactive and Lighthouse scores

These tests are too resource-intensive and time-consuming for developers to run locally as part of a unit test suite, and running them on developer laptops produces results that are not representative of production conditions.

The SETI team’s solution was a tool called PRBuilds.

How PRBuilds Works

1. PRBuilds triggers automatically every time a GitHub Pull Request is updated.
2. It builds and runs the application in a production-like configuration inside a sandboxed environment, isolated from real user traffic.
3. The configured test suite runs against this ephemeral environment.
4. Results are posted back to the Pull Request as a comment, giving the author immediate, contextual feedback.

The result is that developers receive production-quality performance and conformance feedback at the earliest possible stage — before code is merged, and certainly before it reaches users.

Key Takeaway

Shifting performance and integration testing left — into the Pull Request lifecycle — surfaces regressions early without requiring developers to run heavyweight test suites locally.

Goals and Outcomes of the SETI Model

The Guardian’s SETI model is designed to achieve three things:

1. Embed quality into culture and tooling from the ground up. By sitting inside product teams, SETIs help engineers build quality practices into their daily workflows rather than treating QA as an external checkpoint.
2. Gain organisation-wide insight. By coordinating across teams, SETIs can identify systemic quality problems, surface common pain points, and develop reusable solutions and standards that benefit the whole engineering organisation.
3. Avoid becoming a release bottleneck. The model explicitly rejects the gatekeeper model. SETIs are there to accelerate quality, not to slow down delivery.

What This Means for Your Organisation

The Guardian’s approach is not the only valid model, but it illustrates some broadly applicable principles for engineering organisations operating in a microservices and continuous deployment context:

• Quality ownership must be distributed. No single team can test a distributed system comprehensively from the outside. Quality must be built into each team’s development process.
• Test signals should match the deployment environment. Developer laptops are not production. Invest in tooling that provides realistic, automated feedback against production-like environments as early as possible in the development lifecycle.
• Production monitoring is a quality tool. Observability of live features using real usage data catches classes of bugs that pre-release testing routinely misses.
• Specialist engineers who focus on the development process — not just the product — add disproportionate value. Investing in internal tooling, CI/CD infrastructure, and developer experience has compounding returns across every team that benefits from it.

Original source: The Guardian Engineering Blog (theguardian.com). The examples and role descriptions in this article are attributed to The Guardian’s engineering team.

Why Cloud Investment Kept Growing Despite Economic Headwinds

IT budgets are historically among the first casualties when economic uncertainty looms. Yet heading into 2020, the data told a different story. Research firm Gartner forecast that business IT spending in India would grow by approximately 9% in 2020, up from around 6% growth in 2019. That acceleration pointed to a broader structural shift: cloud and automation were no longer discretionary investments but core operational infrastructure.

Analysts projected that more than 80% of enterprise workloads would move to cloud environments by the end of 2020. Rather than signaling a plateau in adoption, this milestone represented the moment cloud computing became the default platform for enterprise IT — not the exception.

Five Major Cloud Shifts Heading Into 2020

While no technology forecast is guaranteed, the following five trends had clear momentum and strong structural drivers. Understanding them remains relevant because they continued to accelerate well beyond 2020.

1. Cloud Security Moves to Centre Stage

Several high-profile public cloud data breaches in 2019 forced enterprises to revisit their security posture. Up to that point, cloud security conversations had been dominated by relatively surface-level controls: identity and access management (IAM) policies, network segmentation and basic compliance checklists.

As organizations migrated increasingly sensitive and regulated workloads to public cloud platforms, the security conversation broadened to include:

• Cloud workload protection platforms (CWPPs) — runtime security for containers, VMs and serverless functions.
• Data encryption at rest and in transit — including customer-managed encryption keys (CMEK).
• Threat intelligence integration — correlating cloud telemetry with known threat actor behaviour.
• Privileged access management (PAM) — tightening just-in-time and least-privilege access to cloud control planes.
• Shared responsibility clarity — ensuring teams understood exactly which security obligations fell to the cloud provider versus the customer.

The shared responsibility model, long documented by providers such as AWS, Azure and Google Cloud, became a practical training priority rather than a footnote in vendor agreements.

2. Edge Computing and Cloud: Complementary, Not Competing

Edge computing distributes data processing to the point where data is generated or consumed — a factory floor sensor, a retail point-of-sale terminal, a connected vehicle — rather than routing everything back to a central data centre. On the surface, this appears to conflict with cloud computing’s centralised model.

In practice, the two are complementary. Edge nodes handle latency-sensitive, high-frequency processing locally. Cloud infrastructure handles aggregation, long-term storage, model training, orchestration and global policy management. A practical example:

• An IoT sensor on a manufacturing line detects an anomaly and triggers a local shutdown in milliseconds (edge).
• The anomaly event is streamed to a cloud data lake for root-cause analysis and predictive maintenance model retraining (cloud).

As edge deployments scaled up heading into 2020, a reliable and well-architected cloud backbone became a prerequisite rather than an optional add-on. Frameworks such as AWS Outposts, Azure Stack Edge and Google Anthos were direct responses to this demand.

3. Hybrid Cloud Matures Into a Genuine Operating Model

On-premises data centres were not disappearing — and for many regulated industries and large enterprises, they never would entirely. The forecast suggested that close to 50% of Indian enterprises would adopt a hybrid cloud model in 2020, a significant jump from previous years.

The historic barrier to hybrid adoption was a lack of consistency: applications behaved differently on-premises than in the public cloud, operational tooling diverged, and networking complexity created latency and reliability problems. The maturation of hybrid cloud platforms began to dissolve these barriers by providing:

• Unified control planes — a single management interface for on-premises and cloud resources.
• Consistent Kubernetes environments — enabling workloads to run and be managed identically across locations.
• Predictable latency — keeping latency-sensitive workloads close to users or data sources while offloading elastic compute to the public cloud.
• Consistent security policies — applying the same identity, network and compliance controls across environments.

The practical result was that enterprises no longer had to choose between agility and control — a hybrid model could, when properly implemented, deliver both.

4. AI and ML Find a Home in the Cloud

Artificial intelligence and machine learning investments had been rising for years, but many projects remained fragmented: isolated proof-of-concepts, siloed data sets and inconsistent infrastructure. The problem was rarely ambition — it was platform fragmentation.

Cloud platforms addressed this directly by offering managed ML services that abstracted infrastructure complexity: data ingestion pipelines, distributed training clusters, model registries and inference endpoints. This allowed data science and ML engineering teams to focus on model quality rather than cluster management.

According to a survey cited in the original analysis, approximately 67% of IT professionals at the time believed AI and ML would be the primary drivers of cloud adoption in 2020. Whether or not that specific figure proves precise, the directional trend was accurate: cloud providers competed aggressively on AI and ML tooling, and managed ML platforms (Amazon SageMaker, Azure Machine Learning, Google Vertex AI) became significant adoption drivers.

5. Serverless Computing Moves Toward the Mainstream

Serverless computing — where the cloud provider dynamically allocates and manages the underlying compute resources in response to events — had existed since AWS Lambda launched in 2014. By 2020, it had matured from an experimental pattern to a production-grade deployment model.

The core value proposition of serverless is developer productivity: teams define functions and event triggers, and the platform handles provisioning, scaling and availability. This eliminates entire categories of operational overhead.

Gartner projected that more than 20% of global enterprises would have deployed serverless technologies by 2020, compared to roughly 5% in 2018. Paul Johnston, co-founder of ServerlessDays, described serverless as “the next iteration of cloud” — a characterisation that captures how fundamental the abstraction shift is.

Common serverless use cases that gained adoption heading into 2020 included:

• Event-driven data processing pipelines (S3 triggers, message queue consumers).
• API backends with unpredictable or spiky traffic patterns.
• Scheduled automation tasks replacing cron-based scripts on dedicated instances.
• Real-time stream processing for IoT and analytics workloads.

What These Trends Tell Us About Cloud Architecture Today

Looking back from the vantage point of a few years later, the five trends above proved durable. Cloud security matured into a dedicated discipline (Cloud Security Posture Management, zero-trust networking). Edge and cloud integration became standard in manufacturing, retail and telecommunications. Hybrid and multi-cloud became the dominant enterprise model. AI/ML workloads became one of the primary justifications for cloud migration. And serverless adoption grew steadily, complemented by container-based abstractions like AWS Fargate and Google Cloud Run.

For DevOps and platform engineering teams, these trends have concrete architectural implications: security must be embedded in pipelines and platform design, not bolted on. Workload placement decisions — edge, on-premises, or cloud — must be deliberate and repeatable. And abstraction layers (serverless, managed Kubernetes, managed ML) exist to eliminate undifferentiated heavy lifting so teams can focus on product outcomes.

The structural forces that drove these trends in 2020 have only intensified since.