Hi everyone, today our topic is related to the software development lifecycle. In other words, what is the life cycle followed during software development? Everything we do in our day-to-day life has a lifecycle involved in it knowingly or unknowingly, we are following a lifecycle or a pattern. For example, we make tea or coffee everyday morning to power our day. What is the procedure for making tea boiling water adding milk adding tea powder adding some sugar? These are the following steps that everybody follows during the time of tea making. The method in which the ingredients are added is the life cycle of tea making process. Let’s see. What are the possible life cycles one can follow here some people boil the water first add milk add tea then add sugar that he is ready? For some others, the order is to add milk and water together bring it to boil then add some tea and then sugar. The rest part of them is in boiling water, add tea brew it well then add some milk to the taste, and then add sugar. So, we saw here the lifecycle involves the steps, which is adding a water milk tea powder for sure. The order in which you do? It is based on your preference. If you want to honor a no sugar diet or a diet or a diabetic person adding sugar becomes an optional step in this life cycle. But no matters what life cycle we follow all of us aim for just one thing a refreshing drink to get you started for the day. Similarly, when developing software. There are numerable steps to develop an ideal quality robust software. These ways of things have been identified and proven to work by experienced teams across the globe. So, let’s go through these seven inevitable processes with a software development lifecycle. Step number one is called planning,

Planning: Planning is where the stakeholder’s business team project manager’s program manager’s technical leads everyone sits together and plans on what needs to be built in simple terms the scope of the application and the milestones by which the software should be ready.

Step number two is

Analysis: Analysis where business analysts on the project will analyze the requirements and teachers that are expected out of the software in detail.

Step number three is

Design: This is the stage where the architects and technical team get involved. They participate in the designing process and decide the architecture of the solution.

Step number four is

Implementation: Finally, it is time to start developing the development team gets on it and starts building the software

Step number five is

Testing: Testing the software gets through a testing team cycle where they start finding defects or bugs. These all defects and bugs get assigned back to the development team, they fix and give them back for retest or testing. This cycle continues until the software is ready for deployment.

Step number six is

Acceptance: Once good quality software is ready. The software is ready to stakeholders for their approval if they are happy with it that means it’ll go signal the software can be deployed for being used by the end customers.

Step number seven and the last one after the deployment scene is called the maintenance base part

Maintenance: This is the part where the deployment team and the operations team. Always make sure that the software is running without bugs and always have been running in the environment.

So, these are the seven steps that we saw involved in the software development lifecycle.

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SAS has been named a leader in the 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms for the eighth year in a row.

The report analysed vendors based on their completeness of vision and ability to execute. It states, “To achieve fully mature, advanced analytic capabilities, organisations must plan for and invest in the end-to-end data science life cycle.

“The lifecycle includes processes for accessing and transforming data, conducting analysis and building analytic models, operationalising and embedding models, managing and monitoring models over time to reassess their relevancy, and adjusting models to reflect changes in the data and business environment.”

According to SAS, its position as a leader is thanks to its Visual Data Mining and Machine Learning platform, which is a visual and programming interface build on the SAS Viya engine.

The company states that SAS Visual Data Mining and Machine Learning supports users of all skill levels to conduct end-to-end data mining and machine learning processes, so that they can handle all tasks in the analytics lifecycle in a way that is simple and automated.

The SAS Viya Platform, which is cloud-native software that enables organisations to leverage analytics, was released in February. The company says the platform is designed for continuous delivery and updates.

“SAS Viya helps organisations see their data, customers and operations in new ways that encourage confident decision making,” explains SAS CEO and founder Jim Goodnight.

“Organisations expect to accelerate innovation and reduce costs when they operationalize analytics.”

The company says that SAS Viya provides the missing layer between models developed in multiple languages and the systems where they’re deployed, eliminating the need for time-consuming recoding to deploy models.

At launch, SAS Viya was made available on the Microsoft Azure cloud platform, building on the companies’ strategic partnership announced last year.

Microsoft vice president of global ISV sales, Casey McGee, says the innovation will provide customer experience and analytics that can enable businesses to make decisions faster.

“The partnership we have with SAS has helped our customers accelerate growth and expand their digital transformation initiatives,” says McGee.

SAS AI strategist Susan Kahler adds, “Across the globe, organisations are aspiring to operationalise analytics and accelerate artificial intelligence adoption to facilitate better, more intelligent business decisions.”

“SAS advanced analytic solutions, which offer innate integrations with popular open source tools, give users at all levels of an organisation access to agile, powerful advanced machine learning to help master the complex data that’s in cloud environments.”

Shipa Corp., a startup that has built a cloud-native application management framework for managing the full app development lifecycle, is emerging from stealth mode today after banking $3.75 million in seed funding round led by Engineering Capital and Jump Capital.

Shipa is trying to make life easier for developers that build containerized applications based on Kubernetes. Software containers are used to host the components of modern apps that can run on any infrastructure, while Kubernetes is the open-source software that’s used to manage them.

The company aims to provide a more “application-centric” way of managing each app throughout its development lifecycle. It says this would be helpful, as developers these days have to spend hours contending with multiple infrastructure components.

That time could be better spent writing code and building new apps. Shipa says developers also devote a great deal of time to tasks such as writing custom scripts, YAML files and Helm charts that are used to configure and package their apps.

Shipa says its application management framework frees developers from these tasks by integrating into developers’ existing continuous integration/continuous development workflows and performance monitoring tools. That abstracts away the underlying infrastructure that hosts their apps.

Essentially, it’s a pre-built framework that can be used by any app built on Kubernetes. It creates and deploys application objects automatically, and it can also monitor the performance of these components to keep a lid on any problems. The framework also creates all of the Kubernetes objects and configuration files each app needs and automatically deploys them to the Kubernetes clusters that need them.

Besides writing new apps, one of the main benefits of Shipa’s framework is it can help to speed up the migration of existing legacy apps to Kubernetes. Companies often spend months creating new frameworks for these kinds of apps, but Shipa says it can help them to start getting value from Kubernetes almost immediately.

Shipa founder and Chief Executive Bruno Andrade said he was inspired to build a comprehensive application management framework after experiencing “crippling challenges” with deploying, managing and fixing cloud native applications.

“With Shipa, we are redesigning how cloud native applications are deployed and operated, helping organizations leverage Kubernetes’ benefits faster and at scale,” Andrade said. “We have taken what developers know are best practices for application delivery on Kubernetes, but built them natively into the Shipa framework so that they now become automatically part of every deployment pipeline.”

Constellation Research Inc. analyst Holger Mueller told SiliconANGLE startups such as Shipa are at the center of most innovation around Kubernetes.

“Shipa makes it easier and faster to build Kubernetes-based applications, and that’s a win for enterprises because it fuels acceleration,” Mueller said. “It’s a win for developers too, because it increases developer velocity.”

What Is Responsible Machine Learning?

As machine learning moves out of research environments and into production systems, organizations face growing scrutiny over how their models make decisions. Bias, opacity, and lack of accountability are no longer just academic concerns—they carry real regulatory, reputational, and ethical weight.

Microsoft Azure’s Responsible Machine Learning framework addresses these challenges by providing toolkits and platform capabilities that help developers understand, protect, and control ML models throughout their entire lifecycle. The three core pillars of this framework are interpretability, fairness, and accountability.

Real-World Context: Fraud Detection at Scandinavian Airlines

The practical value of these capabilities is illustrated by a use case involving Scandinavian Airlines (SAS). During the COVID-19 pandemic, SAS suspended flight operations—but that didn’t stop bad actors from targeting its EuroBonus loyalty program.

With legitimate travel paused, scammers attempted to exploit EuroBonus by pooling points from fraudulent or ghost accounts to book travel rewards or resell tickets. This threatened both SAS’s revenue and the ability of genuine customers to redeem their rewards.

To counter this, SAS built a fraud detection system on Azure Machine Learning. The system ingests real-time data streams covering flights, transactions, and reward activity, feeding thousands of parameters into an ML model designed to flag suspicious behaviour patterns.

Pillar 1: Model Interpretability

A fraud detection model is only operationally useful if analysts can understand why the model flagged a particular transaction. Black-box predictions create two problems: analysts can’t act confidently on unexplained alerts, and regulators may require justification for automated decisions.

Azure Machine Learning addresses this through its interpretability capability, powered by the open-source InterpretML toolkit. InterpretML enables developers and data scientists to:

• Identify which features most strongly influence model predictions (global interpretability).
• Explain individual predictions—for example, why a specific EuroBonus account was flagged as suspicious (local interpretability).
• Build confidence among business stakeholders and compliance teams by surfacing the model’s reasoning.

In the SAS scenario, InterpretML helped analysts understand the specific behavioural signals—such as point pooling patterns from ghost accounts—that the model weighted most heavily, enabling faster and more defensible fraud investigations.

Pillar 2: Fairness

Machine learning models trained on historical data can inadvertently encode and amplify existing biases, producing outcomes that discriminate against certain groups. This is a critical concern in high-stakes applications such as credit scoring, hiring, and fraud detection—where a false positive can significantly harm an individual.

Azure Machine Learning integrates the Fairlearn toolkit to help developers assess and mitigate unfairness in their models. Fairlearn provides:

• Fairness assessment dashboards to visualize how model performance varies across demographic groups.
• Mitigation algorithms that allow developers to trade off predictive accuracy against fairness constraints.
• Integration with the same InterpretML workflows used for interpretability, creating a unified responsible AI pipeline.

Addressing fairness is not just an ethical imperative—it is increasingly a compliance requirement in jurisdictions with AI regulations and anti-discrimination laws.

Pillar 3: Privacy with Differential Privacy Tools

Responsible ML also requires protecting the privacy of individuals whose data is used to train models. Azure provides a differential privacy toolkit, available as open-source software on GitHub, that allows developers to derive statistical insights from datasets while providing mathematical guarantees that no individual’s private data is exposed.

Differential privacy works by introducing carefully calibrated statistical noise into query results or training processes. The key benefit is that organizations can publish aggregate analytics or train models on sensitive data without compromising individual privacy—a capability directly relevant to sectors such as healthcare, finance, and telecommunications.

Pillar 4: Accountability Through MLOps

Interpretability and fairness tools are valuable during model development, but accountability must persist across the entire model lifecycle—from training and validation through deployment and ongoing monitoring.

Azure Machine Learning’s MLOps (Machine Learning Operations) capability provides the infrastructure for this. Key features include:

• Audit trails: Automatic logging of model versions, training runs, datasets used, and evaluation metrics, giving organizations a complete record of how each model was built and validated.
• Automated pipelines: Repeatable, version-controlled workflows for building, training, and deploying models reduce manual error and ensure consistency.
• Compliance support: The audit trail helps organizations demonstrate to regulators that their models meet applicable governance requirements.

Together, these capabilities mean that when a model makes a consequential decision, there is a documented, traceable record of every step that produced it.

Summary: Azure’s Responsible ML Toolkit at a Glance

Why This Matters for DevOps and ML Engineering Teams

Responsible ML is not a feature you add at the end of a project—it needs to be embedded into your ML engineering workflow from the start. For DevOps teams managing ML infrastructure on Azure, this means:

• Incorporating InterpretML and Fairlearn evaluations as gated steps in your CI/CD pipelines for models.
• Using MLOps audit logs to satisfy internal governance reviews and external regulatory audits.
• Treating differential privacy as a default when handling personally identifiable or sensitive training data.
• Documenting model cards and datasheets alongside pipeline artifacts to maintain accountability across team transitions.

As AI regulations mature globally, teams that build responsible practices into their workflows now will face significantly lower compliance costs and risks in the future.

The Continuously Evolving DevOps Role

Change is built into the DNA of DevOps. What started as the merging of Development and Operations teams has since expanded far beyond those original boundaries. Job titles have shifted from IT System Administrator and “Functional Operations” to full ownership of CI/CD production environments. Today, DevOps engineers carry broader responsibility than ever: shortening the systems development lifecycle, ensuring continuous delivery, maintaining high software quality, and increasingly, influencing business outcomes directly.

Industry trends show DevOps teams taking on measurable ownership of KPIs that impact business results. This places DevOps squarely at the intersection of business strategy, software development, and operational infrastructure. At that intersection, infrastructure performance has emerged as the next major challenge to solve.

Crossing the Chasm: Elite DevOps Teams Lead the Way

According to the DORA State of DevOps report (2019), the share of “elite” DevOps teams grew from 7 percent to 20 percent of all surveyed DevOps teams. These elite teams distinguish themselves by selecting the right monitoring tools, applying best practices, and directly influencing both organizational and business performance metrics.

A defining characteristic of elite teams is their heavy reliance on cloud infrastructure and automation. The same report noted that elite teams demonstrated significantly higher cloud utilization compared to lower-performing teams, with automation spanning the full pipeline: code release, deployment, migration, testing, monitoring, and beyond. This automation-first mindset enables CI/CD pipelines that are stable, high-throughput, and highly available—while also improving staff productivity.

The Missing Piece: Continuous Performance Optimization

Despite these advances, research points to performance optimization as one of the most important—and most neglected—areas in modern DevOps pipelines. The rapid pace of feature development and functionality changes often pushes performance engineering to the back of the queue. This creates a tension: DevOps teams aim to shorten development cycles and meet business goals faster, but skipping performance optimization can undermine both objectives.

Currently, many organizations treat performance as a siloed concern rather than an integrated pipeline stage:

• SPE (Software Performance Engineering) is applied on the development side to model and predict performance before deployment.
• APM (Application Performance Monitoring) is used on the operations side to observe live application behavior after deployment.

The gap between these two approaches means performance is measured reactively rather than managed continuously. The next stage in DevOps maturity requires bridging this gap with a continuous performance process—one that automates measurement of response time, throughput, and resource utilization at every stage of the CI/CD pipeline, not just in production.

What’s Holding Performance Optimization Back?

According to an industry survey on Performance in DevOps (cited in the original ITPROportal article), the primary barrier to wider adoption of performance optimization is tooling complexity. Performance engineering approaches available today are often too heavyweight to be practical for teams already managing complex pipelines. For performance optimization to become a first-class citizen in DevOps, tools must be:

• Lightweight — minimal overhead on existing workflows and infrastructure
• Easy to integrate — smooth interoperability with existing CI/CD toolchains
• Automated — reducing or eliminating the need for manual configuration after each deployment

Performance Optimization Approaches: A Comparison

Several classes of performance optimization tools exist today, each balancing cost/performance tradeoffs differently:

1. Resource Management and Workload Orchestration

These tools automatically match workloads to available resources, handling provisioning, placement, and sizing. Kubernetes resource requests and limits are a familiar example of this category. They are well-established but often require manual tuning and may not adapt quickly to real-time workload shifts.

2. Configuration Tuning Optimization

Configuration tuning tools automate parameter modification across the full technology stack to identify the best-performing configuration for a given workload. While powerful, these tools often need to be reconfigured after each deployment, adding operational overhead and potentially slowing down the pipeline.

3. Application-Driven Resource Management Optimization

This is described as the newest approach and is positioned as the most DevOps-friendly. Operating autonomously and in real time—with no changes required to application code—these tools adapt to specific workload demands as they occur. They apply AI-driven continuous optimization at the OS and kernel level to:

• Maximize quality of service (QoS) by increasing throughput rates
• Reduce cloud and on-premises infrastructure costs by improving server utilization and removing idle capacity
• Require no manual reconfiguration after deployments

Because these tools operate below the application layer and integrate into the existing environment without code changes, they present the lowest barrier to adoption for DevOps teams.

Why Performance Optimization Belongs in the CI/CD Pipeline

Integrating performance optimization into CI/CD pipelines—rather than treating it as a post-deployment concern—delivers compounding benefits across the stack:

• Faster feedback loops: Performance regressions are caught earlier, when they are cheaper to fix.
• Cost control: Right-sizing resources continuously avoids both over-provisioning (waste) and under-provisioning (degraded experience).
• Better customer experience: Lower latency and higher throughput directly translate to user satisfaction and retention.
• Business KPI alignment: DevOps teams can demonstrate measurable impact on revenue-affecting metrics, strengthening their organizational influence.

The Next Stage: Closing the CI/CD Performance Gap

The maturation of DevOps now demands that performance optimization be treated as a continuous, automated discipline—not an afterthought or a quarterly audit. With lightweight, AI-assisted performance tools that integrate cleanly into existing pipelines, DevOps teams are better positioned than ever to:

• Streamline end-to-end performance engineering without specialist overhead
• Balance cost and performance tradeoffs in both cloud and on-premises environments
• Demonstrate measurable ROI through reduced infrastructure spend and improved service quality
• Operate with the agility and confidence that high-performing DevOps organizations require

Performance is no longer just an infrastructure concern—it is a business concern. As DevOps teams take on increasing ownership of outcomes that matter to the business, building continuous performance optimization into the pipeline is one of the most impactful investments they can make.

Originally sourced from ITPROportal, August 2020. Rewritten and expanded for a DevOps engineering audience.

Overview

Puppet released its first State of DevOps: Industry Report Card in January 2020, expanding on its eighth annual State of DevOps survey. Based on nearly 3,000 responses from technical professionals across multiple industries, the report benchmarks how different sectors are progressing in two key dimensions: overall DevOps maturity and the integration of security practices into the software delivery lifecycle (often called DevSecOps).

The report was produced by Puppet in collaboration with CircleCI and Splunk. A third-party research firm, OnResearch, hosted the survey and performed the data analysis. Splunk contributed analysis and commentary; all other content was authored by Puppet and CircleCI.

Top-Level Findings

• The technology industry leads all sectors in both DevOps maturity and security integration across requirements, design, build, and test phases.
• The financial services and insurance sector scored the lowest on security integration despite handling highly sensitive data — a finding the report attributes partly to the complexity and volume of regulatory requirements constraining DevOps evolution.
• The retail industry surpasses even technology firms in on-demand deployment capability.
• Government agencies reported the slowest remediation times for critical vulnerabilities.
• The telecom industry showed the fastest year-over-year growth in DevOps maturity but also the highest reported friction between security and delivery teams.

Industry-by-Industry Breakdown

Technology

The technology sector sits at the top of the DevOps maturity curve and leads on security integration across the full software delivery lifecycle. Notable data points from the report:

• 35% of technology companies view security as a shared responsibility across all teams — above the cross-industry average of 31%.
• 28% of technology respondents say leadership always supports DevOps initiatives, the highest of any industry surveyed.

Financial Services and Insurance

Financial services and insurance organizations have a solid DevOps foundation — the sector has the largest proportion of companies rated “Medium” on the DevOps evolution scale — but struggle to advance further. Key challenges include:

• The sector has the fewest organizations rated “High” on DevOps maturity, suggesting a ceiling effect tied to regulatory burden.
• Only 17% of respondents strongly agree that their audit process helps minimize business risk — the lowest figure of any industry, compared to a 24% overall average.
• Security integration scores are the lowest across all sectors, a counterintuitive result given the sensitivity of financial data.

Telecom

Telecom is a sector in rapid transition. The report highlights significant year-over-year improvement alongside a persistent cultural challenge:

• The number of telecom companies scoring in the “High” DevOps maturity category rose 42% compared to the prior year’s survey.
• Despite this progress, telecom has the highest level of friction between security and delivery teams, with 19% of companies reporting collaboration challenges.

Retail

Retail stands out for deployment speed and vulnerability remediation — areas where consumer-facing pressure to ship frequently appears to be driving operational excellence:

• 57% of retail firms are capable of deploying to production on demand.
• 28% report that they are actively deploying on demand — the highest rate across all industries, surpassing even technology firms.
• 53% of retail respondents remediate critical vulnerabilities in under one day, the fastest rate of any sector.

Government

Government agencies face the steepest challenges in vulnerability remediation speed, and security integration is highly polarized:

• Only 3% of government respondents can remediate critical vulnerabilities in under one hour.
• 24% can remediate in under one day — the slowest remediation profile in the report.
• Security integration shows little middle ground: 43% report significant or full integration, while 42% report no or minimal integration.

The Security Integration Imperative

A consistent theme across all sectors is that security integration into DevOps pipelines — sometimes called shifting security left — remains uneven. Alanna Brown, Sr. Director of Community and Developer Relations at Puppet, summarized the challenge in the report announcement:

“Integrating security into your DevOps practices can be challenging, but when done correctly is proven to pay off. Security should not be an afterthought; it must be a shared responsibility across teams during every stage of their software delivery lifecycle.”

Alanna Brown, Sr. Director Community and Developer Relations, Puppet

The report’s cross-industry average of 31% for organizations treating security as a shared responsibility indicates that the majority of companies still operate with security siloed from delivery teams — a structural gap that directly affects remediation speed and overall risk posture.

Report Methodology

The survey targeted technical professionals with direct knowledge of their organization’s IT operations and software delivery processes. Key methodological details:

• ~3,000 responses collected as part of Puppet’s eighth annual State of DevOps survey.
• Survey hosted and data analysis conducted by OnResearch, an independent third-party research firm.
• Report authored by Puppet and CircleCI; Splunk contributed analysis and commentary on findings.

Takeaways for DevOps Practitioners

The Industry Report Card format is useful because it moves beyond the generic DevOps maturity model and surfaces sector-specific constraints. For teams planning DevOps or DevSecOps initiatives, the findings suggest several practical considerations:

• Regulatory environments slow maturity progression. Financial services organizations should invest specifically in automating audit evidence collection and compliance reporting rather than treating compliance as a manual, delivery-blocking process.
• On-demand deployment is achievable outside the technology industry. Retail’s results demonstrate that deployment frequency is driven by operational investment, not solely by the nature of the software being built.
• Security team friction is a cultural and tooling problem. Telecom’s high friction score alongside strong maturity growth suggests that DevOps evolution alone does not resolve security collaboration issues — explicit DevSecOps practices and shared tooling are needed.
• Remediation speed is a leading indicator. Government’s low remediation rates reflect infrastructure and process constraints that DevOps automation could directly address, even within compliance-heavy environments.

The full 2019 State of DevOps: Industry Report Card, including deeper per-industry analysis and recommendations, was made available by Puppet at the time of the announcement. Readers should check Puppet’s official resources site for the current availability of the report.

The following sections walk through each phase of a typical DevOps lifecycle and explain how real-time messaging adds value at each step.

1. Plan

Your messaging platform is the system of record for team conversations related to planning new products and features. Teams collaborate on ideas, debate technical approaches, and reach decisions — all within a searchable, persistent history that keeps stakeholders informed and aligned.

• Document sharing: Roadmaps, diagrams, and presentations can be shared directly in a channel, reducing context-switching to email or file servers.
• Threaded conversations: Threaded replies keep planning discussions organized and easy to reference later.
• Planning tool integrations: Integrations with tools such as Jira let teams surface issue details, create tickets, and update statuses without leaving the messaging interface.
• Video conferencing: When a text conversation needs to become a voice call, integrations with platforms such as Zoom or Webex make it simple to start a meeting from within the channel and return to the chat thread afterwards.

2. Code

As developers write code, they naturally turn to each other for collaboration and support. Whether the team is co-located or fully remote, a shared virtual workspace enables real-time problem-solving and peer review.

• Code snippet sharing: A messaging platform with language syntax highlighting lets developers paste a code block directly into a conversation and see it rendered with correct formatting, making reviews faster and clearer.
• Source control integration: Integrations with platforms such as GitLab or GitHub allow developers to receive pull request notifications, review diffs, and merge code without leaving their messaging workspace.
• Async collaboration: Persistent message history means remote team members working across time zones can pick up a conversation exactly where it left off.

3. Build

Integrating your messaging platform with your build system makes the build process transparent and actionable without requiring engineers to switch between tools.

• Slash commands: Custom slash commands let team members trigger a build, check its status, or retrieve artifact information directly from the messaging UI.
• CI tool plugins: Plugins for tools such as Jenkins expose job status, build logs, and failure alerts inside the team channel, so engineers are notified immediately when a build breaks.
• Automated notifications: Build success or failure messages posted to a dedicated channel ensure the right people are informed without manual follow-up.

4. Test

The testing phase requires close collaboration as teams validate new releases, identify defects, and fix bugs under time pressure. A well-integrated messaging platform can serve as a centralized testing hub.

• Webhooks: Use webhooks to connect CI pipelines and bug-tracking tools to your messaging platform, automatically posting test results, failure alerts, and bug reports to the appropriate channel.
• Triggered test runs: Teams can trigger automated test suites from within the messaging interface using bot commands or slash commands, removing the need to access a separate CI dashboard.
• Real-time visibility: Everyone involved — developers, QA engineers, and product managers — can watch test results appear in real time, reducing the communication lag between finding and fixing a defect.

5. Release

At release time, the messaging platform acts as a real-time broadcaster, ensuring that developers, operations engineers, and business stakeholders all know when a build has shipped and whether it was successful.

• Automated release notifications: Integrating release packaging tools such as Jenkins into your release workflow enables automatic notifications to the right channels when a release is packaged and ready.
• Cross-team transparency: Developers can accelerate follow-on work knowing a release has landed, while ops engineers can prepare for the incoming deployment without waiting for a manual handoff message.
• Audit trail: The persistent message history creates an informal audit trail of who was notified and when, which can be useful during post-release reviews.

6. Deploy

Following code to its final destination in production, the DevOps team needs visibility into which servers received the deployment and any relevant metrics, so they can troubleshoot issues quickly.

• Deployment tool integrations: Whether your team deploys using Puppet, Kubernetes, AWS services, or another solution, your messaging platform can receive automated deployment event notifications and surface them in a dedicated deployment channel.
• Rollout tracking: Automated messages can report the progress of a rolling deployment — for example, how many nodes have received the update — giving the team real-time confidence in the rollout.
• Rapid incident response: If a deployment triggers an error, the alert can surface immediately in the same channel where the deployment was announced, collapsing the gap between detection and response.

7. Monitor

Monitoring is where real-time messaging arguably delivers its greatest operational value. Posting alerts, health metrics, and incident updates directly into a messaging channel transforms how teams respond to production issues.

• Monitoring tool integrations: Tools such as Nagios, Datadog, or PagerDuty can be configured to post alerts and performance data directly to designated channels, making application health visible across the organization — not just to the on-call engineer.
• Incident response channels: When an outage occurs, a dedicated incident channel can be created instantly to bring together the right engineers, share diagnostic data, and coordinate a resolution — all with a preserved log of the incident conversation for the post-mortem.
• Historical data: Posting periodic metric summaries to a channel gives both engineers and business stakeholders a shared view of system health trends without requiring access to a separate monitoring dashboard.

Choosing the Right Integrations

The tools mentioned above — Jira, GitLab, Jenkins, Puppet, Kubernetes, Nagios, Zoom — are illustrative examples of a broader ecosystem. When evaluating a messaging platform for your DevOps environment, prioritize the following capabilities:

• A rich integration marketplace or support for custom webhooks and slash commands
• Persistent, searchable message history
• Granular channel and notification controls to prevent alert fatigue
• Support for code syntax highlighting
• Enterprise-grade security and compliance features appropriate for your industry

When a messaging platform is woven into every stage of the DevOps lifecycle, it becomes far more than a chat application. It becomes the connective tissue that keeps distributed teams aligned, accelerates incident response, and provides the transparency that continuous delivery demands.

Originally sourced from sdtimes.com (2019).

High-profile organisations have been standardising on Kubernetes as their container orchestrator. Tinder announced it was migrating its infrastructure to Kubernetes, followed shortly by Twitter announcing its own move from Mesos to Kubernetes. The momentum is unmistakable—but so are the security challenges that come with it.

To understand trends in container and Kubernetes security and adoption, a survey was conducted with over 200 IT security and operations decision-makers in November 2018. A follow-up survey was then conducted in July 2019 with nearly 400 individuals across security, DevOps, and product teams. The findings below draw from both surveys and illustrate how rapidly the landscape is shifting.

Kubernetes Adoption Grew by 50% in the First Half of 2019

Kubernetes was originally built by Google, drawing on lessons learned from internal cluster management systems Borg and Omega. It was open-sourced in 2014 as a platform for automating the deployment, scaling, and management of containerised applications. Google later partnered with the Linux Foundation to form the Cloud Native Computing Foundation (CNCF), which now stewards the Kubernetes project.

An early and dramatic demonstration of Kubernetes’ scalability came in 2016 when Niantic launched Pokémon Go, built on Kubernetes and deployed on Google Container Engine. At launch, user demand in the US was approximately 50x the original estimate and 10x the worst-case projection. Thanks to Kubernetes’ horizontal scaling capabilities, Niantic was able to stabilise the service and successfully launch in Japan just two weeks later—despite Japan generating three times the US traffic load.

Since that milestone, Kubernetes adoption has accelerated significantly. In the November 2018 survey, 57% of respondents said they were using Kubernetes as their container orchestrator—already the leading choice over any other platform. By July 2019, that figure had climbed to 86%, representing a 50% relative increase in just six months.

While all major cloud providers now offer managed Kubernetes services—AWS EKS, Azure AKS, and Google GKE—a significant portion of users still opt for self-managed clusters. The primary motivation is flexibility: self-managed Kubernetes gives teams greater control and makes it easier to port workloads between environments without being locked into a specific managed offering.

Security Concerns Grow in Lockstep with Adoption

Security concerns remain one of the primary constraints on container and Kubernetes adoption. 2019 alone saw the discovery of several high-severity vulnerabilities, including a critical flaw in the runC container runtime, a Kubernetes privilege escalation vulnerability, a denial-of-service vulnerability, and additional issues identified as part of a CNCF-commissioned security audit. Tesla’s public cloud environment was famously compromised via an exposed, unsecured Kubernetes dashboard—a cautionary example of what inadequate container security can lead to.

Survey respondents most frequently cited inadequate investment in security as their biggest concern about their organisation’s container strategy. This reflects a broader challenge: moving to a containerised or microservices architecture introduces new attack surfaces and security considerations that traditional security tooling was not designed to address.

Organisations need dedicated security controls purpose-built for containers, Kubernetes, and microservices in order to meet security and compliance obligations. Unlike traditional waterfall development, modern CI/CD-based methodologies require that security controls be deeply embedded in the pipeline from the earliest stages—not bolted on at the end.

Shifting Left: Security Across the Container Lifecycle

Survey respondents identified runtime as the lifecycle phase they are most worried about. However, most practitioners also understand that runtime failures are frequently the downstream consequence of security gaps introduced during the build and deploy phases. More than half (57%) of respondents expressed greater concern about what happens during build and deploy than at runtime.

This reflects the growing industry consensus around “shifting left”—applying security practices earlier in the development lifecycle rather than waiting until deployment or runtime. Catching misconfigurations, vulnerable base images, or over-privileged containers during the build phase is far less costly than addressing them after an incident in production.

• Build phase: Scan container images for known CVEs, enforce base image policies, validate Dockerfiles for security misconfigurations.
• Deploy phase: Use admission controllers (e.g., OPA/Gatekeeper) to enforce pod security policies, RBAC rules, and network policies before workloads reach the cluster.
• Runtime phase: Monitor for anomalous process behaviour, unexpected network connections, and privilege escalations using runtime security tools.

Containers and Kubernetes Are Running Everywhere

One of the more striking findings is just how heterogeneous container environments have become. While 70% of respondents run at least some containers on-premises, 75% of that group are also running workloads in the cloud. This means that any viable security strategy must span both environments simultaneously.

By mid-2019, more than half of respondents (53%) were operating in hybrid mode, up from 40% at the end of 2018. In the same period, the proportion of organisations running containers exclusively on-premises nearly halved—dropping from 31% to 17%. Cloud-only deployments remained broadly steady over the same period.

On the cloud provider front, AWS continued to lead container deployments, followed by Azure. Google Cloud came in third but showed notable growth, increasing its share from 18% to 28% over just six months—reflecting the company’s significant investment in Kubernetes tooling and the appeal of GKE for Kubernetes-native workloads.

DevSecOps: More Than a Buzzword

Traditional security processes, when treated as a gate at the end of development, become a bottleneck in DevOps workflows. The increasing sophistication of threats facing containerised environments has elevated DevSecOps—the integration of security responsibilities directly into development and operations teams—from a theoretical concept to an operational necessity.

Survey data showed a notable jump in the number of respondents citing DevSecOps teams as the appropriate owners of container security programs. The effect was even more pronounced when filtering for respondents in dedicated security or compliance roles: 42% of those individuals viewed DevSecOps as the right organisational structure for running container security programs. This signals a meaningful shift in how security ownership is being distributed across the engineering organisation.

Final Thoughts

Despite real and growing security concerns, containerisation shows no signs of slowing. The operational advantages—development velocity, deployment efficiency, and the ability to scale workloads dynamically—are consistently outweighing the anxiety around security gaps.

The survey data underscores this momentum. The percentage of organisations running more than 50% of their containers in production grew from 13% to 22%—a 70% increase in just six months. Over the same period, those running fewer than 10% of containers in production fell from 52% to 39%.

The message for engineering and security teams is clear: treat security as a first-class concern throughout the entire container lifecycle—from build through deploy to runtime. Organisations that embed security controls into their CI/CD pipelines, enforce least-privilege principles in Kubernetes RBAC, and invest in runtime monitoring will be far better positioned than those treating security as an afterthought.

Unlocking the full benefits of cloud-native technologies requires protecting the full container lifecycle. In a world where 75% of global organisations are projected to run containerised workloads in production, securing that foundation is not optional—it is foundational.

What Is a DevOps CI/CD Engineer?

A DevOps CI/CD Engineer sits at the intersection of software development, quality assurance, and IT operations. The role focuses on building, maintaining, and improving the pipelines that move code from a developer’s workstation through automated testing and into production — reliably and repeatedly.

Unlike a pure software developer or a traditional sysadmin, a CI/CD Engineer must hold both a developer’s mindset and an operator’s discipline. They automate the repetitive, error-prone steps in software delivery so that teams can ship higher-quality software faster.

Core Responsibilities

While specific duties vary by organisation, a DevOps CI/CD Engineer is typically expected to:

• Design, build, and maintain Continuous Integration and Continuous Delivery pipelines.
• Participate as the DevOps subject-matter expert inside cross-functional development and maintenance projects.
• Manage software configuration and environment consistency across the full system lifecycle — from development through to retirement.
• Collaborate closely with developers, QA engineers, architects, and operations staff to remove delivery bottlenecks.
• Drive automation of build, test, and deployment processes to reduce manual effort and human error.
• Identify and eliminate waste in delivery workflows, applying lean principles to engineering processes.
• Advocate for and implement best practices such as Test-Driven Development (TDD), infrastructure as code, and shift-left testing.
• Monitor pipeline health and system performance, responding to failures and improving reliability over time.

Typical Requirements and Qualifications

The following requirements reflect what employers commonly ask for in a mid-level DevOps CI/CD Engineer role. They are drawn from an industry job description and represent a realistic benchmark for the position.

Education

A Bachelor’s degree in Software Engineering, Informatics, Computer Science, or an equivalent technical discipline is the standard baseline. Relevant industry experience combined with professional certifications can substitute in many organisations.

Experience

• A minimum of three years of hands-on experience in either software development or DevOps engineering.
• At least two years of experience with object-oriented software development principles and practices.
• Demonstrable experience with Agile and Scrum delivery methodologies. Familiarity with the V-model is a plus in regulated or safety-critical environments.

Technical Skills

The role demands a broad but coherent technical skill set spanning multiple languages and tooling categories:

• Programming and scripting languages: C#, C++, Python, Perl, and PowerShell. The ability to read and write in multiple languages is essential for integrating diverse systems.
• CI/CD tooling: Hands-on experience with Jenkins and TFS Build (Azure DevOps) is explicitly valued. Broader familiarity with tools such as GitLab CI, GitHub Actions, or TeamCity is increasingly expected across the industry.
• Test automation: Comfort with frequent, incremental code testing and deployment. Experience with Test-Driven Development (TDD) is listed as a plus.
• Systems and IT operations: Understanding of server infrastructure, networking basics, and environment management is required to own the full delivery chain, not just the build scripts.

Soft Skills and Mindset

• Collaborative by default: DevOps is fundamentally about people and process, not just tooling. CI/CD Engineers must work comfortably alongside developers, testers, product owners, and infrastructure teams.
• Lean and improvement-oriented: Actively looking for waste — unnecessary waiting, rework, manual steps — and driving change to eliminate it.
• Business-outcome focused: Understanding that faster pipelines and higher test coverage exist to deliver business value, not as ends in themselves.
• Adaptable: Willing and able to work with both cutting-edge tooling and legacy systems that may have existed for decades.
• Proactive: Taking ownership of problems and improvements without waiting to be asked.

Domain Knowledge (Advantageous)

Experience in regulated or safety-critical domains — such as healthcare product development — is listed as an advantage. This reflects the additional rigour required for audit trails, change control, and validation that these environments demand from their CI/CD processes.

Why CI/CD Engineering Matters

Continuous Integration and Continuous Delivery are the operational backbone of modern software delivery. Without robust pipelines, even well-written code can sit unreleased for weeks, accumulating integration debt and risk. A skilled CI/CD Engineer shortens feedback loops, catches defects early, and gives the business the ability to release on demand rather than on a fixed, stressful schedule.

For regulated industries — healthcare, finance, aerospace — CI/CD is equally important as a mechanism for enforcing compliance: every change is logged, every test result is recorded, and every deployment is reproducible. In these contexts, the CI/CD Engineer is not just a productivity enabler but a key part of quality and safety assurance.

Is This Role Right for You?

If you enjoy automating repetitive problems, have a broad technical background, and get satisfaction from enabling other engineers to work faster and with more confidence, a DevOps CI/CD Engineer role is a strong career fit. The role rewards generalists who can go deep when needed — someone equally comfortable debugging a failing Jenkins pipeline, reviewing a pull request for testability, and discussing deployment risk with a product manager.

Candidates who thrive in this role tend to be self-directed, systems thinkers who measure success by the outcomes their pipelines enable rather than by the elegance of the tooling they build.

Overview

In early July 2019, VMware instructed users to watch for software patches covering 31 of its products affected by two vulnerabilities in the Linux kernel’s implementation of TCP Selective Acknowledgement (SACK). Left unpatched, these flaws could allow a remote attacker to trigger a denial-of-service (DoS) condition against affected systems.

Background: What Are the Linux SACK Vulnerabilities?

TCP Selective Acknowledgement (SACK) is a mechanism that allows a TCP receiver to inform the sender which segments have been received out of order, enabling more efficient retransmission. Netflix researchers discovered and disclosed two serious flaws in how the Linux kernel handles SACK packets:

• SACK Panic (CVE-2019-11477) — A specially crafted sequence of SACK packets could cause the Linux kernel to panic (crash), resulting in a denial of service. This vulnerability carries a CVSSv3 base score of 7.5 (High).
• SACK Excess Resource Usage (CVE-2019-11478) — Malformed SACK blocks could cause the kernel to fragment the TCP retransmission queue excessively, consuming disproportionate CPU and memory resources. This vulnerability carries a CVSSv3 base score of 5.3 (Medium).

Both vulnerabilities were originally found and responsibly disclosed by Netflix researchers, together with two additional Linux-specific bugs disclosed at the same time.

VMware’s Assessment and Advisory

VMware published a security advisory on July 2, 2019, collectively rating the vulnerabilities as Important in severity. The advisory warned:

“These issues may allow a malicious entity to execute a denial of service attack against affected products.”

VMware Security Advisory, July 2, 2019

Because a large portion of VMware’s product portfolio runs on Linux-based appliances and virtual machines, the attack surface was broad — spanning 31 distinct products.

Products with Patches or Workarounds Available (as of July 3, 2019)

As of approximately 11:30 a.m. ET on July 3, 2019, VMware had released patches for the following products:

• SD-WAN Edge by VeloCloud
• SD-WAN Gateway by VeloCloud
• SD-WAN Orchestrator by VeloCloud
• Unified Access Gateway
• vCenter Server Appliance

Workarounds (rather than full patches) were also made available for:

• Unified Access Gateway
• vCloud Director for Service Providers

Products with Patches Pending

At the time of reporting, the following 26 products were confirmed affected but awaiting patches:

• AppDefense
• Container Service Extension
• Enterprise PKS
• Horizon
• Horizon DaaS
• Hybrid Cloud Extension
• Identity Manager
• Integrated OpenStack
• NSX for vSphere
• NSX-T Data Center
• Pulse Console
• Skyline Collector
• vCloud Availability Appliance
• vCloud Director for Service Providers
• vCloud Usage Meter
• vRealize Automation
• vRealize Business for Cloud
• vRealize Code Stream
• vRealize Log Insight
• vRealize Network Insight
• vRealize Operations Manager
• vRealize Orchestrator Appliance
• vRealize Suite Lifecycle Manager
• vSphere Data Protection
• vSphere Integrated Containers
• vSphere Replication

Recommended Actions for VMware Administrators

VMware administrators should take the following steps if they have not already applied fixes:

1. Check VMware’s Security Advisories page for the latest patch availability for each affected product. Patch timelines varied per product.
2. Apply available patches immediately for products where patches were released, prioritising internet-facing appliances.
3. Apply workarounds where full patches were not yet available. A common Linux-level workaround for SACK vulnerabilities is to disable SACK via the kernel parameter net.ipv4.tcp_sack=0, though this should only be applied after verifying VMware’s specific guidance for each product, as it may affect performance.
4. Monitor VMware’s advisory for updates as patches for pending products become available.
5. Restrict network access to management interfaces where possible, reducing the exposure of vulnerable appliances to untrusted networks.

Context: Industry-Wide Impact

The Linux SACK vulnerabilities affected any system running an unpatched Linux kernel, meaning the impact extended well beyond VMware. Major Linux distributions — including Red Hat, Ubuntu, CentOS, and SUSE — issued kernel updates around the same time. Cloud providers, hypervisor vendors, and network appliance manufacturers all issued their own advisories. The breadth of VMware’s affected product list reflects how widely Linux-based appliances are used across its portfolio.

This article is based on reporting from SC Magazine UK and VMware’s July 2019 security advisory. Administrators should consult the official VMware Security Advisories portal for current patch status, as all products listed above have since received updates or reached end-of-life.