Your Guide to Observability Engineering in 2024

It may sound complicated and daunting, but so much of observability is about discovering the unknown unknowns in your critical systems.

The capabilities of observability engineering can help you make those discoveries.

Most organizations have some form of monitoring, alerting and troubleshooting, which can be adequate to a point but fall short when trying to determine the root cause of unexpected outages. Observability engineering, on the other hand, provides a swift means to evolving process and tooling to uncover the reasons behind these issues. 

Using this approach, teams can more effectively query their telemetry data, visualize anomalies, isolate peculiarities, spikes and bottlenecks, and then explore possibilities to solve them. In fact, observability engineering was specifically designed to tackle these unique, one-off incidents within the context of today’s complex cloud environments.

What exactly does it take to embrace observability engineering in 2024, and how can you harness its power for your organization? We’ll get into that shortly, but let’s first set some parameters for exactly what we mean when we talk about full stack observability.

Definition and Scope of Observability

Observability is defined as the ability to measure the internal states of a system by examining its outputs. It’s how modern organizations approach the process of discovering issues with a given service, understanding their nature, and determining the best course of action for resolution.

There’s a common misconception that observability and monitoring are synonymous, but that’s not the case. Observability extends the concept of monitoring by not just detecting when something goes wrong but by providing the necessary data to understand why and how it happened – hopefully before it impacts production systems. 

The scope of observability specifically encompasses the collection, analysis, and visualization of telemetry data, including metrics, logs, and traces and other signals. This holistic view allows teams to diagnose issues more efficiently and ensure systems are running as expected. This is especially necessary when there are potentially thousands of events in a given service that need to be analyzed and understood for proper, functioning observability.

Key Components of Observability

The key components of observability are often defined as the “three pillars” of telemetry data—logs, metrics and traces. But those signals do not, in and of themselves, make up the components of observability, nor do they mean that by solely looking at and analyzing that data you are truly executing an observability practice.

Instead, you utilize those components as part of an overall observability data correlation strategy that must also include other critical components such as continuous profiling, business metrics, CI/CD pipeline performance and interactions with and feedback from customers.

The Role of an Observability Engineer

There is no one definition of the role of an “observability engineer.” In our space, we see quite a few different titles for customers with these roles and responsibilities—these include site reliability engineers (SREs), platform engineers, DevOps engineers, system architects, software engineers and more.

In any event, an observability engineer is someone who is responsible for building, maintaining, monitoring and/or observing data pipelines, and working with the involved telemetry data (see the aforementioned components of observability). 

The observability engineer needs to know how to analyze and interpret the data provided by systems. At the very least, they need to know the right questions to ask about the status of systems and what if any measures need to be taken to correct any issues that materialize. 

Challenges in Observability Engineering

Observability engineers are required to wear many hats in an organization, from managing and understanding systems to troubleshooting and problem-solving some of the most critical issues that can come up for any cloud-centric business.

Selling the business case for observability can be a significant challenge for any observability champion in an organization driven by a breadth of issues ranging from questions about its overall impact to potential costs. This can involve advocating for technology that will advance observability goals, or for a mindset shift that will enable better processes around the concept.

Specific challenges in these areas include:

Data overload. One of the primary challenges in observability is managing the sheer volume of data generated by modern systems. It can be difficult to filter out noise and focus on the most relevant information. Observability engineering aims to tackle this issue.

Complexity of distributed systems. As systems become more distributed, understanding the interactions between components becomes increasingly complex. Ensuring end-to-end observability across multiple services and platforms can be a significant challenge for observability engineers.

Tool integration. Integrating various observability tools and ensuring they work seamlessly together requires careful planning and execution. Incompatibilities and integration issues can hinder the effectiveness of many observability solutions.

Best Practices in Observability

A proven set of best practices for observability engineering is critical for any organization to follow. These include processes, technology and ensuring you have the right people and expertise in place to ensure success. 

This list is by no means exhaustive, but organizations need to consider the following best practices steps to get an observability strategy off the ground:

Define clear objectives. Establish what you want to achieve with observability. What do you want to get out of your practice? What will you be measuring and how will you achieve success? Define specific goals and key performance indicators (KPIs) that align with your business objectives.

Standardize data collection. Implement standardized methods for collecting metrics, logs, and traces across your systems. This keeps the organization aligned and keeps everyone involved on the same page. Consistency is key to effective analysis and troubleshooting.

Automate alerting. Set up automated alerts based on predefined thresholds to ensure timely detection of issues. Use machine learning to reduce false positives and prioritize critical alerts. It’s critical not to set up too many alerts so as to create alert fatigue in your organization—focus on the things you need to alert on and nothing else.

Invest in training. Ensure your team is well-versed in observability tools and practices. Continuous training and knowledge sharing are essential for maintaining effective observability. The world of observability constantly changes, so staying ahead of trends is critical.

Regularly review and refine. Observability for developers and other stakeholders is not a one-time setup. Regularly review your observability practices and refine them based on feedback and changing system requirements.

Benefits of Effective Observability Engineering

When you’ve successfully implemented observability engineering in your organization, the benefits are myriad and lasting. They’ll directly impact your bottom line, and help your business not only bounce back faster from production issues but help prevent them from happening in the first place. 

Improved incident response. The 2024 Observability Pulse survey report showed that 82% of organizations see mean time to resolution (MTTR) from production incidents of over an hour. Effective observability enables teams to quickly identify and diagnose issues, reducing MTTR and minimizing downtime.

Enhanced performance. By monitoring key metrics and analyzing system behavior, teams can identify performance bottlenecks and optimize their systems for better efficiency.

Proactive issue detection. Observability allows teams to detect anomalies and potential issues before they escalate into critical problems, leading to a more stable and reliable system.

Better decision-making. With a comprehensive view of system performance and behavior, organizations can make more informed decisions about architecture, scaling, and resource allocation.

Future Trends in Observability Engineering

The future is here when it comes to observability: most vendors today, including Logz.io, have integrated generative AI into their platforms, alongside long-standing proprietary AI capabilities. 

Generative AI integration is intended to give observability engineers the opportunity to extend their teams and eliminate some tasks to get to the bottom of issues faster. These technologies can help predict issues before they occur and provide intelligent recommendations for remediation.

Some other future trends to monitor in observability include:

Unified observability platforms. The trend towards unified observability platforms that integrate metrics, logs, and traces into a single interface — supporting key use cases such as applications and Kubernetes analysis — is likely to continue. These platforms simplify the observability process and provide a holistic view of system performance.

Increased focus on security. With growing concerns around cybersecurity, observability engineering will increasingly incorporate security monitoring. Detecting and responding to security incidents in real-time will become a critical aspect of observability.

How Logz.io Can Help You Reach Observability Engineering Goals

Effective observability engineering can only be achieved with the right tools and expertise from partners who can help extend your in-house capabilities. That’s what we try to provide at Logz.io through our platform.

Logz.io Open 360™ is a cloud-native observability platform that gives you the tools to visualize, troubleshoot and remediate issues that show up in your telemetry data in future-facing ways to monitor your critical applications and infrastructure. Our platform is intuitive to use and it’s very easy to set up and start shipping your data in minutes.

With Open 360, you’ll meet your observability engineering goals with a platform that helps you:

• Automate querying and interaction with your platform through AI-powered, conversational terms to get to the bottom of issues fast
• Explore logging, metrics and trace data quickly with intuitive, high-performance search filters so you can accelerate troubleshooting and reduce MTTR through visualizations of spikes, dips and other trends
• Quickly drill into individual transactions to diagnose root cause issues
• Get the most out of your open source tools and processes via a unified platform that correlates event data
• Gain full visualization of your environment tailored to specific needs and use cases via pre-built or customizable monitoring dashboards
• Filter out irrelevant telemetry data so you can separate signal from noise and drastically reduce your data management, analysis and storage costs 
• Combine Kubernetes logs, metrics, and traces for unified analysis, troubleshooting and gain automatic contextualization with relevant data organized by node or deployment
• Discover full visibility into application health and performance with an observability-based alternative to traditional APM, alongside automatic service discovery, instrumentation and collection for telemetry data
• Continuously optimize data and cost efficiency to ensure that you focus on and pay for only the telemetry data that matters most to your unique requirements

See how Logz.io Open 360 can help you reach your goals for modern observability, sign up for a free trial today.