Tired of your CI/CD pipeline keeping you in the dark? These questions probably sound all too familiar:

• Where is my pipeline right now?
• Why is my pipeline slow or unstable?
• Which tests are flaky and delaying releases?
• How fast can artifacts reach production?
• Which phase is slowing down my artifacts?

CI/CD pipelines can be complex, and a lack of visibility can create delays, instability, and inefficiency. The Dynatrace platform is like flipping a switch in a darkened room. You can see what’s actually happening, answer pressing questions, and gain insights that can improve performance. Let’s take a look at how Dynatrace can illuminate your pipelines.

Elevating CI/CD with observability: From code to confidence

Here’s how each question maps to a problem and its solution:

Where is my pipeline right now, and why is it slow or unstable?

Challenge: Pipelines can feel like black boxes—teams often don’t know whether they’re stuck during build, waiting on tests, or failing at deployment. Additionally, slow or unstable pipelines miss service-level agreements, frustrate teams, and delay releases due to bottlenecks or resource issues.

Insight: Dynatrace provides real-time visibility into each pipeline stage by using tracing and metrics. It pinpoints performance bottlenecks, detects anomalies, and triggers alerts to enable fast resolution. By analyzing stage durations and resource usage, such as CPU spikes during testing, it helps teams stabilize pipelines and meet deadlines.

As shown in the screenshot below, ingesting SDLC events into an Azure DevOps pipeline helps teams identify issues early and maintain pipeline reliability by enabling visibility into pipeline, execution status, and error-prone tasks.

Which tests are flaky and delaying releases?

Challenge: Flaky, inconsistent tests erode trust and slow down releases as teams spend time debugging false failures.

Insight: Dynatrace identifies flaky tests by tracking execution patterns and failure rates. By tagging test spans with metadata using OpenTelemetry, teams can quickly spot unreliable tests, isolate them, and keep delivery moving smoothly.

As illustrated in the screenshot below, integrating OpenTelemetry with a Jenkins pipeline enables the generation of metrics, logs, and traces. These are ingested via the Dynatrace Collector, providing a comprehensive view that helps teams pinpoint the test executions.

Furthermore, Dynatrace allows teams to drill into spans, logs, and exceptions using context-aware data, offering end-to-end visibility from the originating endpoint all the way to the underlying logs and exception traces, as seen in the example below.

How fast can artifacts reach production?

Challenge: Slow artifact promotion from development to production creates unpredictable release cycles, delaying value delivery.

Insight: Dynatrace measures end-to-end time from commit to deployment. Visualizing the pipeline’s flow with traces helps optimize stages, reduce wait times, and ensure predictable releases.

As seen below, leveraging the GitLab API in combination with Dynatrace Workflows, teams gain real-time visibility into pipeline behavior over time, eliminating uncertainty and improving confidence in delivery processes.

Additionally, Dynatrace predictive AI capabilities proactively inform teams of potential delays or anomalies, enabling earlier intervention and smoother release cycles.

Implement CI/CD pipeline observability with Dynatrace

Consider the following practical techniques to unlock the full potential of Dynatrace within your CI/CD pipeline. Each approach enables visibility into different aspects of the software delivery lifecycle, helping teams monitor, analyze, and optimize their pipeline performance.

1. OpenTelemetry

Instrument your CI/CD pipeline with OpenTelemetry to stream real-time metrics, traces, and logs into Dynatrace. This approach provides comprehensive observability but may require setting up and maintaining collectors.

OpenTelemetry instrumentation examples:

• Jenkins OpenTelemetry example
• GitLab Observability with OpenTelemetry example
• GitHub Actions using OpenTelemetry example

2. Dynatrace Workflows

Dynatrace Workflows automate the periodic retrieval of build and deployment metrics from CI/CD tool APIs. This method is easy to set up and does not require changes to the pipeline itself.

Dynatrace workflow examples for Pipeline Observability

• Jenkins Dynatrace workflow and dashboard example
• Azure DevOps workflow and dashboard example
• Gitlab Dynatrace workflow and dashboard example

3. Custom extensions

Similar to workflows, extensions can query CI/CD APIs and ingest relevant metrics into Dynatrace. They avoid pipeline modifications but require development effort and ongoing maintenance.

4. Pushing SDLC Events

Configure the CI/CD tools to send Software Development Lifecycle (SDLC) events (e.g., build start, success, failure) directly to Dynatrace via the SDLC Events API. Once ingested, these events can be analyzed using Dynatrace Query Language (DQL) to detect patterns, trigger alerts, or correlate with other observability signals.

Alternatively, OpenPipeline can be setup to extract SDLC events from the pipeline logs in tools like Jenkins, ArgoCD, and Github. This enables out-of-the-box pipeline observability by capturing metadata such as application name, pipeline run details, and version, supporting CI/CD analytics, and root cause analysis enriched with lifecycle metadata.

The table below outlines the types of datasets accessible through each technique, helping you choose the right approach based on your observability goals.

Conclusion

Bringing observability into your CI/CD pipelines with Dynatrace helps teams deliver software faster and more reliably. By leveraging techniques like OpenTelemetry, Dynatrace Workflows, and SDLC events, teams gain the visibility needed to continuously improve their delivery process.

What’s next?

In the next post, we will explore how to integrate your CI/CD tools with Dynatrace using these techniques. In the meantime, here are a few examples that show how this works in practice:

• ArgoCD deployment observability
• Azure DevOps pipeline observability
• GitHub Actions pipeline observability
• GitLab pipeline observability clinic video

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According to recent research from TechTarget’s Enterprise Strategy Group (ESG), generative AI will change software development activities, from quality assurance to debugging to CI/CD pipeline configuration.

Many organizations are turning to generative artificial intelligence and automation to free developers from manual, mundane tasks to focus on more business-critical initiatives and innovation projects. Therefore, it’s no surprise that generative AI is poised to have a massive impact by automating software development tasks today and in the near term, according to ESG’s data.

In the research, “Code Transformed: Tracking the Impact of Generative AI on Application Development,” sponsored by Dynatrace, findings indicate that AI and automation are already having a major impact on how developers are working today.

Weighing the pros and cons of automating software development

AI-enabled development can eliminate manual effort and free developers’ time to engage in more strategic, high-level code development. Software development tasks include testing and quality assurance (QA), security, coding, debugging, CI/CD pipeline configuration, and documentation.

On the whole, survey respondents view AI as a way to accelerate software development and to improve software quality. According to the survey, 79% of respondents say AI is already helping to reduce time spent on manual tasks.

At the same time, 75% of respondents say it has taken longer than expected to derive value from AI initiatives related to automating CI/CD pipelines.

How AI is reshaping application development

The ESG report explains how three types of AI are reshaping the app development ecosystem:

• Generative AI leverages large language AI models to create new outputs. These help teams with data augmentation, anomaly detection, simulation, and documentation, among other areas.
• Predictive AI uses data collection, algorithm assignment, and model training for user behavior prediction, demand forecasting, fraud detection, and quality control, among other areas.
• Causal AI models the cause-and-effect relationship between variables to help with areas that include personalization, testing, optimization, policy impact assessment, and more.

AI influences QA and container orchestration

Organizations are also using AI for myriad testing and QA activities, including error detection and debugging (44%), among other tasks.

Generative AI is also becoming key to container orchestration—a process that automates the deployment and management of containerized applications and services at scale.

Organizations use generative AI for myriad use cases involving container orchestration, according to the research, such as automated remediation (35%).

Reaping the rewards of generative AI and automation

Ultimately, the report indicates that IT operations (57%) and product development (42%) stand to benefit most from generative AI.

The trend in automating software development tasks, therefore, stands to benefit the stewards of IT systems and product innovation—two central locations of organizational growth and risk mitigation—today and in the future.

Source: Enterprise Strategy Group, a division of TechTarget, Inc. Research Report, Code Transformed: Tracking the Impact of Generative AI on Application Development, February 2024.

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Continuous delivery demands continuous security. To make continuous delivery possible, observability and security need to go hand in hand.

In today’s rapidly evolving business and technology landscape, organizations often prioritize the speed of development over security. The concern is that comprehensive application security in CI/CD environments is too hard to achieve and would slow down development and delivery.

However, achieving end-to-end application security is possible with the right tools and intelligence. Modern solutions like Snyk and Dynatrace offer a way to achieve the speed of modern innovation without sacrificing security.

AI-driven observability from Dynatrace and the Snyk developer-first security platform empower development and operations teams to prioritize vulnerabilities and respond with full insight into business context and potential impact.

Dynatrace + Snyk: Where observability and security converge for continuous security

According to recent research, 69% of CISOs acknowledge that vulnerability management has become increasingly complex, resulting in inadequate security coverage for many applications.

Balancing the need for security with the rapid pace of application development continues to challenge both development and security teams.

Recently, Snyk and Dynatrace introduced the DevSecOps Lifecycle Coverage app to address this issue and others like it.

This innovative solution combines Snyk Container and Dynatrace observability data to provide comprehensive reporting—highlighting which running containers have undergone Snyk Container scans. The app provides complete visibility into container scanning during development and production, eliminating security blind spots and aiding vulnerability prioritization.

Bottom line: Continuous delivery needs continuous security. Weaving security into the fabric of your DevOps practice prevents breaches and ensures the delivery of secure digital services.

Observability and security by the numbers

Maintaining continuous security is critical for continuous delivery but remains a challenge for most organizations, according to recent research:

• Only 37% of organizations have any runtime vulnerability management, and only 4% have runtime vulnerability management for containers.
• 34% of CIOs say they sacrifice code security to deliver innovation quicker.
• 49% of CIOs focus on testing security in production, but less than 31% look at security in development.

Modern application development does not need to overwhelm your development teams.

Snyk provides a developer-friendly security platform that helps developers find and fix vulnerabilities in every application component. Customers report that Snyk has helped drive substantial ROI in time savings and risk avoidance in the past year — a 2x increase in return on investment from 2022.

Dynatrace provides powerful AI-based observability, putting all your infrastructure, applications, and events in context. Together, Snyk and Dynatrace drive DevSecOps practices and give you end-to-end visibility into the security and risk for your applications from development to production.

Dynatrace + Snyk helps developers build apps securely, efficiently, and in line with their security and operations teams.

Bottom line: Automating security in development will enhance quality, time to delivery, and operational efficiency:

• 44% decrease in mean time to fix vulnerabilities for customers using the Snyk platform.
• 3.2% reduction in critical severity vulnerabilities for enterprise customers.
• 142% increase on average in vulnerabilities (high and critical severity) that each customer organization has fixed in the past year.
• 249% increase in code base coverage on average.
• 2.2 fewer development full-time employees are needed for container maintenance, on average.

Dynatrace + Snyk + AWS: Strengthening your DevSecOps practice with continuous security

Operating on AWS’s cloud infrastructure provides scalability, reliability, and a wide range of services to support modern application development and operations.

Combining Dynatrace, Snyk, and AWS creates a robust ecosystem for developing, deploying, monitoring, and securing modern applications.

AWS provides the cloud infrastructure, Dynatrace ensures application performance and observability, and Snyk enhances security throughout the development lifecycle. This combination helps organizations deliver reliable, scalable, and secure applications in the cloud.

Automating vulnerability management: Meet the experts

Shivam Jindal, a partner solutions architect at Snyk, and Susan St. Clair, principal security solutions engineer at Dynatrace, recently joined AWS’ Matt Girdharry in a panel discussion to unravel the challenges of application security and shed light on the importance of automation in vulnerability management.

1. The merger of observability and security

The panel emphasized the need for merging observability and security. By integrating Dynatrace and Snyk, developers can gain visibility and context throughout the development and operation process. Jindal, a partner solutions architect at Snyk, noted, “Integrating security into the development workflow not only enhances security but also boosts efficiency.”

2. Automating vulnerability management

The experts underscored the importance of automating vulnerability management to ensure secure deployments. St. Clair elaborated, “The Dynatrace platform’s integration with container scanning tools helps in automating security, reducing human error, and speeding up the deployment process.”

3. Leveraging the AWS Marketplace

Girdharry, the worldwide lead of observability and security for AWS Partnerships, highlighted the benefits of utilizing the AWS Marketplace to access tools like Dynatrace and Snyk. “The AWS marketplace simplifies the process of finding, buying, and deploying software, including security solutions like Snyk and Dynatrace. It’s a one-stop-shop for enhancing your cloud security,” he said.

As businesses continue to navigate the digital landscape, these insights will help ensure a secure and efficient development process. The power of Snyk and Dynatrace integration, the emphasis on automation, and the convenience of the AWS marketplace are shaping the future of secure software development.

Discover more about the value of cloud-native observability and security with the following resources.

• Check out our Snyk + Dynatrace + AWS infographic.
• Meet the Experts: Intelligent Cloud Native Security with Snyk and Dynatrace on-demand webinar.

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“Move fast and break things.”

How many times have you heard that? Know what? No one likes that. No one likes breaking things. It’s more like: “Move fast and break things—only because you don’t know how to avoid it.”

Granted, as a soundbite, that is nowhere near as catchy. But progressive delivery is essentially the solution to that problem: it enables you to move fast and avoid breaking things.

How does an organization, especially if they’re not yet doing continuous integration and continuous delivery (CI/CD), either at all or well, “move fast and not break things”? The answer: Progressive delivery with feature flags and observability.

Progressive delivery encompasses multiple methodologies where DevOps teams introduce new features to small user subsets (or cohorts) slowly or gradually in a controlled manner. By doing so, teams can closely observe new functionality and hopefully roll it out to end users more safely and with fewer errors.

Progressive delivery with feature flagging delivers the following benefits:

• Decreases risk
• Provides faster feedback loops
• Decouples the mechanics of deploying from the act of a release
• Increases business agility and confidence

Feature flagging is one method of progressive delivery but there are multiple accepted techniques, for example:

• A/B (or blue/green) releases
• Canary releases
• Feature flagging

You can deliver both canary and blue/green deployments without feature flags. Just copy and paste your code, make changes to the “green” version, and deploy “green” alongside “blue.” Then find some way to direct “some” of your users to “green” and leave everyone else on “blue.”

There are problems with this manual approach, though, for example, the following:

• Manual changes make the tech stack more complex: You also have to manually update routing rules and other changes.
• It’s also a blunt instrument. It’s hard to be selective with what constitutes “some” users. Usually, it ends up being a percentage-based thing.
• You need to pay for, manage, and maintain two copies of the application.
• If you do need to rollback, all new functionality in that release is revoked (even if it still works)

A better way is to organize features and functions using feature flags, which gives you fine-grained control over what to release, when, and to whom.

How feature flagging enables progressive delivery

At their most basic, feature flags are an if/else statement that can alter the application behavior in real-time, at runtime without redeploying an application.

This technical capability brings the following benefits:

• Realtime application behavior changes without code redeployment
• No additional infrastructure required: The application is the same; no additional infrastructure or configuration is required
• Lower cost: No additional infrastructure means no additional cost
• Faster experimentation: Enabling and disabling functionality with the flip of a switch means you can try out many more versions and hypotheses. You spend time experimenting rather than waiting for CI/CD pipelines to run.

Feature flag use cases

You can use feature flagging in lots of situations, but here are a few examples:

Limiting access: For your eyes only

• Most start their feature flag journey with scenarios like these:
• Users in Australia get the new features first, before other countries
• US users see a localized version of the content
• Staff get access to additional features
• Upon login, the “user level” badge is bronze, silver, or gold, depending on the logged-in user
• A subset of users is selected to receive a special offer. The special offer is placed behind a feature flag. The flag is enabled for those users. After a set amount of time, the flag is disabled – the offer is no longer available.

A “deployment” no longer means a “release”

Using feature flags, “deploying” code no longer means “releasing code to end users.” With feature flags, teams can place new features and functionality behind a feature flag that is disabled by default.

This makes deployments almost risk-free and can dramatically increase your code-to-production time.

After all, if you know that by default, no one gets the new code, your deployment is safe. Then when you’re ready, enabling new functionality for only those you choose means that even if something goes wrong, you have the following safeguards in place:

• The blast radius is limited; only a small subset of users are impacted
• You know who it went wrong for and can easily disable the functionality without a redeployment.

Feature flagging with the Dynatrace Platform

The Dynatrace Platform uses feature flags extensively:

• All new functionality is placed behind feature flags. Customers can opt-in to early access programs. Product managers then enable the flags for those customers only.
• Dynatrace clusters are feature-flagged as “stable,” “early,” or “developer” to denote a risk appetite. Updates are pushed first to “developer” clusters. If everything is OK, the “early” clusters receive the update until finally, the “stable” clusters are updated.
• Documentation for new features or “internal only” knowledge is placed behind a feature flag. When logged in, staff have access to documentation that no one else can see.

What if I’m not doing CI/CD yet?

As LaunchDarkly points out, if you’re not doing CI/CD yet, at first glance, feature flagging can seem daunting and “too difficult.”

However, if you can deploy knowing the new code is never seen or used unless intended, why not continuously deliver software?

CI/CD is scary for many precisely because a deployment means a release, and historically, that’s an “all or nothing” activity.

Feature flags give you a safety net, so CI/CD doesn’t have to be scary.

OpenFeature

OpenFeature is an open standard that describes feature flagging. It has already been adopted by many feature flag vendors and adopted by some names you might know.

Companies normally start their feature flag journey by building an in-house solution. This could be backed by a database or a simple JSON file.

Sooner or later though, companies invariably find they’ve outgrown (or no longer wish to maintain) their in-house solutions. The business decides on a feature flag vendor, and the developers get to work.

The developers must:

1. Learn the APIs of the chosen feature flag vendor
2. Remove all existing integration code between the application(s) and the in-house vendor
3. Replace all the above code with new code to “speak to” the feature flag vendor
4. Repeat this process across every application in the enterprise

OpenFeature makes it much simpler for enterprises to adopt feature flags whether they’re using an in-house or commercial flag solution:

• OpenFeature requests flag values in a standard way, regardless of “backend”
• Teams can swap backends easily

To enable feature flagging with OpenFeature, all you have to do is change the following value from

OpenFeature.setProvider(“My-In-House-Provider”)

to

OpenFeature.setProvider(“My-Vendor-Provider”)

The importance of observability

Feature flags and unrestricted continuous delivery bring great benefits, but observability is a critical component of a progressive delivery system. After all, if you can change everything about the system using flags but can’t see what the effects of those changes are, you’re flying blind.

You can configure Dynatrace to capture feature flag values on every distributed trace, so the impacts of those flags can truly be understood at a transaction and individual user level.

Dynatrace makes it easy to see exactly what the issue is and who is impacted.

Example: API traffic with feature flags

Imagine an API endpoint that a service calls to perform an action. This action relies on an algorithm. You’ve enabled feature flags to decide which algorithm a user receives.

Your team wants to introduce and test a new algorithm (which is supposedly faster) and you implement a feature flag to target only a small percentage of logged-in users to test this new algorithm.

To determine whether the new algorithm is actually faster, you must measure its response time and compare it to the response time of the old algorithm. If you only split the response time by endpoint, the statistics for logged out and in would be grouped together – you’d get the average of both algorithms (user groups).

Dynatrace captures the flag values, so splitting by the logged-in or out status is easy.

Dynatrace shows that logged-in users have a feature flag enabled which gives a better response time.

Perhaps it is time to roll out the new algorithm to a higher percentage of logged-in users. By using feature flags, you can roll it out slowly and safely. Dynatrace is there every step of the way, observing and ensuring a safe progressive rollout.

Dynatrace ❤️ progressive delivery with feature flags

Start your progressive delivery journey today with Dynatrace. Sign up for a free trial and start realizing safety-guaranteed progressive delivery with feature flags.

When you have your trial tenant, head over to OpenFeature to get started.

To learn more about feature flags with Dynatrace, see my previous post, Feature flagging done right with Dynatrace and OpenFeature.

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The surge in demand for digital services since 2020 is a trend that is here to stay and only expected to accelerate. To compete, organizations have to achieve both speed and reliability when bringing new products and services to market. To meet this demand, organizations are adopting DevOps practices, such as continuous integration and continuous delivery, and the related practice of continuous deployment, referred to collectively as CI/CD.

CI/CD is a series of interconnected processes that empower developers to build quality software through well-aligned and automated development, testing, delivery, and deployment. Together, these practices ensure better collaboration and greater efficiency for DevOps teams throughout the software development life cycle.

Here’s what you need to know about these software development practices, how they relate to each other, and how they benefit DevOps teams as they optimize and automate more processes to achieve ever-faster time to value for customers.

Continuous integration streamlines development

Continuous integration (CI) is a software development practice that streamlines the internal process of creating software. With CI, multiple software developers can work on different features or modules of the same application and individually commit their updates to a shared code repository as they complete them, often many times a day. When they check in their code, the build management system automatically creates a build and tests it. If the test fails, the system notifies the team to fix the code. This practice helps software teams quickly detect and resolve any bugs that come up during the development process.

Continuous integration also prevents “merge hell,” which can happen when two or more developers inadvertently make conflicting changes that break the build when the lines are merged back into the master branch. Continuous integration also avoids teams having to reconcile substantial amounts of conflicting or redundant code, which can require code freezes or even a dedicated integration stage in the pipeline.

When software development teams continuously integrate incremental changes, each developer is free to make changes without worrying about throwing someone else’s work off track or about their own work being stepped on by another. Smooth, regular merging helps software development teams complete projects more quickly and efficiently. It also ensures there is always a testable, up-to-date build that will properly compile, which is critical for frequent and rigorous application testing.

This approach saves time and resources that would otherwise be spent fixing issues later in the software development life cycle, or worse, after release, when issues are much more difficult to address.

Continuous delivery ensures code is always ready to deploy

Continuous delivery (CD) is a process in which DevOps teams develop and deliver complete portions of software to a repository, such as GitHub or a container registry, in short, controlled cycles. Continuous delivery seeks to make releases regular and predictable events for DevOps staff, and seamless for end-users. Another goal of continuous delivery is to always keep code in a deployable state so updates can go live at a moment’s notice with little or no issues.

DevOps teams can automate CI/CD pipelines to move code through the appropriate environments with no human input, which accelerates the build, test, and deployment stages of software development — or any additional stages they have in place, depending on the processes they use.

For example, when a feature is ready for client demonstration, the DevOps team can have a CD tool automatically deploy it to a test server so the client can see how it works and provide feedback before it is released to the production server.

Continuous deployment ensures customers always have the latest

Continuous deployment (also CD) is an extension of continuous delivery in which builds that pass testing are automatically deployed directly to production environments on a daily, or even hourly, basis.

Continuous deployment hastens the feedback loop with your customers and eases the burdens on operations teams by automating the next stage in the pipeline.

Once an organization’s continuous delivery practices are stable and mature, they often adopt automated continuous deployment and testing in a tiered “blue-green” sequence. In this scenario, the new build (green) is deployed in parallel with the existing build (blue) to ensure it works before the blue build is retired. Teams can also use a phased “canary” deployment approach, where the new build is gradually phased in to replace the existing build.

Continuous delivery vs. continuous deployment: which is it in CI/CD?

Continuous delivery (CD) often gets confused with continuous deployment (CD). If CD stands for continuous delivery and continuous deployment, which is it when people refer to CI/CD practices?

In short, it’s either continuous delivery, or continuous delivery and continuous deployment. Although related, the two terms refer to automated processes that happen at different points in the delivery workflow.

You can think of adopting these practices as three steps in a continuum of DevOps automation:

1. Continuous integration to streamline development and automate building and testing. It’s a way for developers to work on the same code base at the same time.
2. Continuous delivery to automate the process of delivering completed code blocks into the main branch, where it can be deployed to a production environment by the operations team. It’s a way for the development part of a DevOps team to automate the process of testing and committing code.
3. Continuous deployment to automate the process of deploying completed, tested code into a production environment. It’s a way for the operations part of a DevOps team to automate the process of deploying new code to customers.

When an organization first adopts agile DevOps practices, they often start out with continuous integration, and mature quickly into continuous delivery, thus achieving CI/CD. Many organizations stop here, preferring to release production code manually. Others progress into continuous deployment so they can automate the entire software development, delivery, and deployment pipeline.

The benefits of CI/CD

The main benefit of continuous integration and continuous delivery is that it reduces the time required to develop applications and features. This gives companies a competitive advantage over businesses that still take a manual approach to software development.

Besides the competitive advantage, CI/CD enables agile development teams to constantly iterate and release new features. These practices ensure everyone is working with the same version of the code as they develop and refine features and functions. With the logistics of integrating and testing builds automated, engineers can focus on what they do best: coding.

If teams have implemented continuous deployment, code can spend less time waiting for testing and deployment and more time in production, which accelerates feedback from users and, ultimately, improves business outcomes.

Increased efficiency, reduced time to market, and quicker innovation make CI/CD an attractive proposition for organizations of all types.

The drive to optimize and automate more CI/CD processes

To meet competitive demands, organizations know they must release new products and services more quickly than ever without compromising quality or reliability. They know basic CI/CD practices are fundamental to DevOps and DevSecOps initiatives that look to develop applications with more effective collaboration and greater precision.

Teams that implement CI/CD practices successfully rely on many tools and methods to manage features, versions, testing, and builds. Automation happens at every stage of the pipeline, from building, packaging, and testing to pushing applications to different production environments.

To accelerate the development pipeline for ever-faster releases with less risk, teams need continuous automation and advanced AI-driven observability across all the tools in their DevOps toolchain so they can automate manual steps and identify quality issues earlier in the software lifecycle.

If your organization uses or is considering adopting CI/CD DevOps processes, a best practice is to implement a full-stack observability platform that can provide code-level visibility of all software builds, apps, and services in your environment, whether they’re in development or deployed to end-users.

The Dynatrace Software Intelligence Platform continuously and automatically monitors the performance of DevOps tools, and seamlessly integrates with CI/CD workflows. With improved collaboration on a single platform and a shared data model, you can ensure your entire team has continuous situational awareness across the lifecycle.

To take a deeper look at continuous delivery, see my next post, What is continuous delivery and what are best practices for implementing it?

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