Continuous monitoring and observability transform DevOps pipelines from basic automation to engines of innovation and reliability. Building on foundational pipeline concepts and best practices for delivery and testing, this blog post reveals actionable strategies for integrating monitoring and observability to achieve faster releases, fewer incidents, and greater business impact.

In brief:

• A DevOps pipeline automates building, testing, and deploying code, minimizing human error and streamlining software delivery.
• Continuous monitoring and observability are crucial for ensuring system reliability, optimizing performance, and proactively detecting issues throughout the entire pipeline, not just in production.
• Integrating monitoring and observability early in the CI/CD pipeline enhances deployment speed, reduces incidents, and maximizes business impact.
• Many organizations overlook the value of continuous monitoring and observability in application development, limiting their potential benefits.

Most companies fail to recognize the importance of continuous monitoring and observation for their DevOps pipelines. However, they are foundational to system reliability, performance optimization, and proactive issue detection. They also boost network security. Unfortunately, many confine these practices to the networking world without realizing the critical role continuous monitoring and observability can play in application development.

In reality, continuous monitoring and observability can significantly improve the efficiency of your continuous integration and continuous delivery (CI/CD) pipeline. And when used correctly, they smooth the deployment process by pinpointing issues long before the app goes to production.

Let’s dive into what continuous monitoring and observability are in a DevOps context and how they drive pipeline excellence.

What Is the DevOps Pipeline?

A DevOps pipeline is a set of automated processes and tools that streamlines the process of building, testing, and deploying code to produce a production-ready application. Practical DevOps pipeline tools automate as much of the process as possible, reducing the risk of human error disrupting operations.

That’s essential because even if developers write perfect code, they could still make a mistake during compilation or dependency installation. Pipeline tools automate executing these tasks and tests to create a more efficient DevOps CI/CD pipeline.

The Role of Continuous Monitoring and Observability

Traditionally, monitoring has focused primarily on production environments after code has been deployed. However, modern DevOps practices advocate for integrating observability instrumentation and telemetry collection much earlier in the development life cycle rather than treating it as a postdeployment afterthought.

Monitoring after code deployment may seem logical because you want to monitor and observe issues in the app as it approaches production readiness. But relegating monitoring and observability to a mere add-on at the tail end of your pipeline may cause you to miss opportunities to boost speed, reliability and business value.

By understanding the role of continuous monitoring and observability in the context of early-stage development, the benefits become clear.

Continuous Monitoring and Observability in the DevOps Pipeline

A healthy DevOps pipeline performs the following:

• Delivers Software Rapidly: This is often measured by deployment frequency (DF), which refers to the frequency at which the pipeline deploys code. An effective pipeline can even deploy code multiple times per day, when necessary. But DF is merely a vanity metric unless validated by change failure rate (CFR).
• Produces Reliable Software: Teams measure reliability using CFR, which is the percentage of deployments that require remediation, such as a rollback, hotfix, or patch. A high DF needs a low CFR to indicate pipeline health.
• Results in Secure Software and Deployment Processes: Your pipeline doesn’t introduce threats through exposed application programming interface (API) keys, third-party dependencies, weak identity and access management (IAM), or other issues.

Continuous monitoring and observability support a healthy pipeline by verifying the quality and security of your apps. They involve collecting and analyzing performance metrics (monitoring) and understanding how your app and environment interact (observability).

Here’s another way to look at it: Monitoring tells you that something is wrong by tracking known metrics and thresholds, while observability helps you understand why it’s wrong by letting you explore your system’s behavior through logs, traces, and metrics, even for issues you didn’t anticipate.

Monitoring can be proactive and reactive:

• Proactive monitoring is preventive because it uses predictive analytics and mitigation measures to prevent issues before they arise.
• Reactive monitoring occurs after an incident, such as checking logs to determine why a crash occurred or waiting until a server runs out of disk space before taking action.

For example, suppose you’re building a solution in Kubernetes, leveraging its flexible, cloud-based development environment. You’re using a monitoring tool, such as Prometheus, to track how your app allocates and consumes memory. Simultaneously, you use Grafana Tempo, an observability solution, to capture distributed traces as various components of the app interact.

While running a new microservice that connects to multiple APIs, Prometheus signals a spike in memory consumption. Not good. But the good news is that your observability tool, Grafana, tracks the memory usage spike to the API calls your microservice is making.

Thanks to continuous monitoring and observation of your DevOps environment, you have pinpointed a potentially serious memory issue and identified its cause. You can now take steps to avoid it, well before sending the app to production.

Continuous monitoring and observability work like teammates to help you catch issues early, which is essential for developers because it enables them to build higher-quality products. At the same time, monitoring and observability also improve the speed and reliability of your DevOps pipeline.

How Continuous Monitoring and Observability Improve DevOps Speed and Reliability

By continuously monitoring your builds, you ensure the code you release performs as intended. By observing your environment, you can pinpoint precisely how to optimize performance and address any issues that arise. More effective code results in more reliable products, and addressing problems early in the dev process improves speed by preventing a cascade of issues that could’ve stemmed from each problem you detect and fix.

For example, let’s say you use OpenTelemetry to instrument a microservice that frequently makes calls to several APIs. OpenTelemetry exports traces and metrics that can be scraped by Prometheus or sent to other monitoring back-ends, like Grafana Cloud.

Prometheus alerts you about excess latency in the microservice. Grafana visualizations make it easy to pinpoint specific API latency issues. You then find an alternative API that produces lower latency and continue building the microservice.

Now, imagine what would happen if you didn’t use continuous monitoring and observability to find and address the latency issue.

Suppose three other components in your app depend on the microservice you’re building, and each component connects to multiple APIs. During testing, the team notices latency plaguing the app. This is unacceptable because it needs to produce data in real time to be effective for end users.

Your team may spend a few hours or longer reviewing every API call to identify which one is causing the latency. They may even end up scurrying down several rabbit holes to investigate other options. They spend a day pointing fingers at the user interface (UI) framework, blaming repositories like those from Flutter or React for the issue.

They could also unfairly blame the database because some may feel they should’ve paid a little more for a faster database.

Or they could add the container to the blame game, perhaps because some suspect one or more containers should have more memory.

Three days later, when frustration is high and patience is low, the team realizes it was the API all along.

With continuous monitoring and observability, you could have prevented confusion and delay while still delivering high-quality software at scale.

Beyond performance and reliability, integrating security scanning into your pipeline — while distinct from observability — provides similar early-detection benefits.

More Secure Products, Faster: Why to Integrate Security Scanning Into Your CI/CD Pipeline

Integrating security scanning into your CI/CD pipeline can enhance the security of end products by detecting issues early and facilitating easier resolution by security team members.

While DevOps has at least lowered the barrier that used to exist between development and security teams, it’s still easy to toss a “complete” iteration at the security folks with a casual, “Can you check this for issues, please?” Monitoring and observability can tear down any remnants of silos or communication walls that remain by producing security data throughout an app’s development.

Incorporating security scans into every build phase is one way to achieve this goal.

For instance, you can use OWASP Dependency-Check to continuously scan for known common vulnerabilities and exposures (CVEs) in your Azure pipeline. Alternatively, you can use Microsoft Defender for Containers to scan for vulnerabilities and Azure Policy or Azure Container Registry Docker Content Trust to ensure images come from approved repositories.

The benefits of speed and reliability, achieved through continuous monitoring and observability, are clear and diverse. Let’s explore some best practices for integrating them into your process.

4 Best Practices for Integrating Monitoring and Observability Into DevOps Processes

Building continuous monitoring and observability into your DevOps pipeline involves cultural, technical and operational adjustments. Here are some best practices that make it easier:

1. Use a Shift-Left Approach

You should integrate monitoring and security testing early in the DevOps life cycle – this is called a shift-left approach. For instance, you can set up a test environment and use it to assess how code changes affect API response times.

2. Establish Clear Goals

Your continuous monitoring and observing need to support goals aligned with business outcomes, not just “greater efficiency.” For instance, you can set a goal to reduce failures during alpha test deployment by 40 percent.

3. Build a Monitoring and Observability Culture

Encourage your programmers to integrate monitoring and observability tools into their testing processes as early in the pipeline as possible. They can use integrated development environment (IDE) plug-ins to run automated tests and performance checks, for instance.

4. Break Down Silos Between Programmers

To tear down silos, you can use a unified observability platform that all programmers share. You can also encourage communication and collaboration through lunch-and-learn meetups where people discuss their roles and skill sets.

Train Your Team on Continuous Monitoring and Observability

Training should be high on your list of best practices because it helps team members feel comfortable with what may be a new and intimidating way of building software.

Your programmers may feel they’ve built dozens of practical, stable, secure solutions in the past and not fully appreciate the value of continuous monitoring and observability, seeing it as “just more work.” This is where training makes a big difference.

Some key training topics can include:

• Fundamental, foundational lessons on core principles, including the pillars of observability (metrics, logs, traces and events) and the philosophy of shared responsibility
• How to use your observability tools, such as Grafana, Datadog and Prometheus
• How to use infrastructure as code (IaC) to add consistency and simplicity to your monitoring and observing
• How to get the most out of internal knowledge repositories. Team members should feel comfortable adding to and gleaning information from your organization’s knowledge base, particularly around monitoring and observability tools and best practices.

Training your team helps them understand the value of continuous monitoring and observability throughout the DevOps pipeline. However, when it comes time to invest, decision-makers often struggle to take action, though justifying the expense is relatively straightforward.

How to Justify the Cost of Continuous Monitoring and Observability to Executives

Making the case for investing in a monitoring and observability system hinges on showing return on investment (ROI) in the time saved. Depending on the number of monitored virtual servers and gigabytes of logs ingested, you could easily end up spending a few hundred to even thousands of dollars every month.

For example, IBM’s Instana Observability costs $75 per monitored virtual server (MVS) at the Standard Tier, plus $0.35 per gigabyte of logs. It’s easy to see how costs can quickly pile up.

To justify the expense, outline the following:

• The average amount of time the team spent in the last quarter hunting down issues that a monitoring and observability solution could have pinpointed in minutes.
• How many times the team has missed a production deadline because of preventable issues
• Feedback metrics, perhaps collected during a final debrief or scrum session, that show room for improvement in programmer satisfaction. For example, if a critical mass of developers indicates that the issue mitigation process “needs improvement,” that fact alone could compel a decision-maker to invest in a continuous monitoring and observability solution.

Using DevOps metrics when designing your observability and monitoring systems gives your teams tangible, measurable standards of success.

In addition to deployment frequency (DF) and change failure rate (CFR), discussed earlier, from a monitoring and observability standpoint, you should use the four golden signals (originally defined in Google’s Site Reliability Engineering Framework) to gauge the performance of each deployment:

• Latency: Latency is the time it takes for a request to be fulfilled from the user’s perspective. Since latency must account for the user’s device, you may need to factor in network speed and bandwidth as your teams assess latency.
• Errors: Your error metrics focus on the frequency of failed requests. A failure can include either a complete failure to render a response or the return of incorrect data.
• Saturation: Saturation is a measurement of how close the system is to using up all of its resources, such as storage, memory, and central processing units (CPUs) or virtual CPUs (vCPUs).
• Traffic: Traffic refers to the demand your system must handle, such as the number of requests per second. If, during monitoring, you discover excess traffic, you should address it because it can impact latency, errors, and saturation as well.

Make Your DevOps Pipeline More Efficient With Continuous Monitoring and Observability

Integrating continuous monitoring and observability into your DevOps pipeline is more than a technical enhancement. It’s a strategic investment in your team’s efficiency. By proactively addressing issues, you save time and money.

You also weave a culture of continuous improvement into your DevOps fabric. These solutions have a price tag, but the efficiency savings and reliability improvements more than offset the costs.

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