What is Observability?

As technology systems become more complicated, the teams that manage them face growing challenges in keeping track of and addressing problems across different cloud environments. Due to this, the teams responsible for operations, development, and system reliability seek better visibility and understanding of these diverse and intricate computing setups. They need simpler ways to monitor and identify the issues within these complex systems.

Table of Content

• What is observability?
• Difference between monitoring and observability
• Why is observability important?
• Benefits of observability
• How do you make a system observable?
• Why the three pillars of observability aren’t enough
• What are the challenges of observability?
• The importance of the single source of truth
• Making observability actionable and scalable for the IT Department

What is Observability?

Observability means we can understand how a system works based on the information it produces, like logs, measurements, and traces. As cloud systems have become more complicated, observability has become more important. It’s harder to find why something goes wrong or doesn’t work as expected.

Since cloud services are spread out and constantly changing, observability also refers to the tools businesses use to make sense of how their cloud systems are performing. These tools help them understand the information coming from the cloud and identify any issues.

How Observability Works?

Observability relies on the information collected from various parts of your cloud and infrastructure. Each component like hardware, software, cloud, containers and tools generate logs of what they do. Observability helps you understand what happens within these systems, so you can locate issues and keep your systems running and customers satisfied.

Implementing Observability

Most organizations implement observability using tools or services like Open Telemetry which is open source. Organizations also use observability platforms to detect and analyze incidents that impact their operations, software, security, or user experiences. As teams get familiar with observability data, they realize this advantage extends beyond IT into the entire company.

Some think observability just means advanced monitoring, but there are key differences between these two. Monitoring checks a system’s vital signs, while observability gives a deeper understanding of the system’s inner workings and behavior to explain why something is happening.

Difference Between Monitoring and Observability

Monitoring

In monitoring, you set up dashboards in advance to alert you about performance issues you expect to happen. However, these dashboards assume you can predict what problems will occur before they happen. Cloud systems are constantly changing and complex, so it’s difficult to know what kinds of problems may arise.

Observability

With observability, teams have tools that provide complete data about the environment. This allows you to flexibly explore what’s happening and quickly find the root cause of issues, even if you couldn’t anticipate them.

Observability means having logs, metrics, and traces. But in complex cloud environments, observability also needs to include metadata, user behavior, system mapping, and code-level details. This broader view helps you understand and resolve issues better.

Why is Observability Important?

Understanding Unknown Problems

Modern cloud environments are constantly changing and becoming more complex. Teams can’t know or monitor most problems in advance. This question of “unknown issues” is about observability that allows you to continually learn new kinds of problems.

Automating Operations and Security

Observability is a key component of AIOps. As more enterprises move towards cloud environments, they are finding ways of using AIOps for automating such processes as monitoring, testing, delivery, security and incident response. By using AI to collect and analyze data across all systems, your organization can reliably automate these tasks.

Improving User Experiences

The significance of visibility as a concept is not limited to IT alone. When you collect and inspect observability data, this will enable you to know how your digital services lead to profit. With this view, you can optimize your conversations, ensure software releases achieve the objectives of the business and make sound judgments regarding what is important.

In cases where user experience data is evaluated alongside observability solutions, one could avoid any problems users may find later on as well as develop better user experiences that are based on genuine feedback.

Benefits of Observability

1. Application Performance Monitoring

Organizations can identify and fix application performance issues related to cloud and microservices quickly through full observability. Observability solutions also automate processes, making operations more efficient and helping application teams innovate faster.

2. DevSecOps and Site Reliability Engineering (SRE)

Observability is a essential part of applications and infrastructure, not just tooling. Developers design applications with observability in mind so they can observe the software as intended. With observable data, DevSecOps and SRE teams can build better, more reliable, secure applications throughout the software lifecycle.

3. Monitoring Infrastructure, Cloud, and Kubernetes

Infrastructure and operations teams can use observability solutions to better monitor on-premises, cloud, and Kubernetes environments in one place. This improves application availability, performance, and quicker issue resolution. It helps identify cloud latencies, optimize resources, and improve infrastructure and Kubernetes management.

4. End-User Experience

Good user experiences increase customer satisfaction, loyalty and business reputation. By catching potential issues before users notice, organizations can optimize the user experience through real-time understanding of what users go through.

5. Business Analytics

Combining application analytics, performance data and business context allows real-time monitoring of business impact, conversion rates and service level compliance. This ensures software releases meet business goals.

6. DevOps and DevSecOps Automation

Observability helps developers better understand applications to automate testing, continuous integration and deployment. This enables faster release of high-quality code and improves productivity through effective collaboration. These advancements drive innovation and enhance user interfaces for better end-user experiences.

How do you Make a System Observable?

1. Logs: Text records of events that happened at a given point in time.

2. Metrics: Metrics refer to values or measurements which are mostly calculated over a period of time and can come from infrastructure, hosts, services, cloud platforms and external sources.

3. Distributed Tracing: By following the path of a transaction or request through applications it shows how services connect together and provides code-level details.

4. User Experience: An application’s user experience data extends traditional observability with the outside-in perspective from a particular digital experience even in pre-production environments.

Why are the three Pillars of Observability Aren’t Enough?

The Importance of Open Source Solutions

Open source solutions establish a standard way to gather information in cloud environments. These increase visibility into cloud applications and help developers and operations teams consistently understand how healthy an application is across different platforms.

Role of the Real User Monitoring (RUM) and Synthetic Testing

Real-user monitoring allows organizations to gain a real-time view of the user experience by tracing a request’s path as it interacts with services. The team can watch this through synthetic monitoring or by recording real user sessions. These capabilities extend data by adding information about APIs, third-party services, browser errors, user details, and application performance from the user’s perspective.

With real-user monitoring, IT, DevSecOps, and SRE teams can see the complete end-to-end journey of a request and gain real-time insight into system health. They can proactively troubleshoot issues before they impact performance, easily recover from failures, and better understand the user experience.

Don’t Forget Overburdened Teams

While organizations have good intentions and strategies, they often overestimate the ability of already overburdened teams to constantly observe, understand, and act upon a large amount of data and insights. Although observability comes with complex challenges, organizations that overcome these challenges will find it valuable.

What are the Challenges of Observability?

• Data Silos: Understanding the interdependencies across applications clouds and digital channels like web, mobile and IoT is difficult due to multiple agents, different sources of data, siloed monitoring tools.
• Data Overload: It is almost impossible to make sense of the vast volumes of raw data collected from all components in dynamic cloud environments such as AWS, Azure and Google Cloud Platform (GCP) as well as Kubernetes and containers.
• Manual Instrumentation: IT resources dedicate the majority of their time to establishing observability rather than acting on insights when they need to manually instrument and modify code for every new component or agent.
• Lack of Pre-Production: Developers cannot see how actual users will impact applications and infrastructure before pushing code into production even with load testing.
• Troubleshooting Inefficiency: Telemetry data across multiple tools and vendors makes no sense; teams lose valuable time trying to identify root causes for problems.
• Multiple Tools: There are many systems that could impact performance so one single tool might not give full observability across all application systems.

The importance of the single source of truth

Organizations need a single source of truth to gain complete observability across their application infrastructure and accurately identify the root causes of performance issues. When organizations have a single platform that can handle cloud complexity, capture all relevant data, and analyze it with AI, teams can instantly identify the root cause of any problem, whether it’s in the application itself or the supporting architecture.

• Turn large amounts of data into real answers, instead of having IT teams piece together an understanding from scattered data sources.
• Gain crucial insights into areas of the infrastructure they might not have otherwise been able to see.
• Work collaboratively and accelerate the troubleshooting process, allowing the organization to act faster than with traditional monitoring tools, thanks to enhanced awareness.

Making observability actionable and scalable for the IT Department

1. Understand Context and Topology

Understand how applications and infrastructure are connected by identifying relationships and dependencies among all components across potentially billions of interconnected parts. Gather rich contextual data that allows real-time topology maps showing dependencies across stacks, services, processes, and hosts.

2. Implement Continuous Automation

Automatically discover, instrument, and baseline every system component continuously. This shifts IT effort away from manual setup to value-adding innovation projects focused on understanding what matters. Observability becomes “always-on” and scalable, allowing constrained teams to do more with less.

3. Establish True AIOps

AI-driven fault analysis combined with code-level visibility enables teams to automatically pinpoint the root cause of issues without time-consuming manual efforts. AI can also automatically detect unusual changes to discover unknown problems teams are unaware of. These actionable insights drive faster, more accurate responses.

4. Foster an Open Ecosystem

Observability considers external open-source data sources like OpenTelemetry guided by vendors. Automated discovery, instrumentation, and topology mapping support platforms seeking scalable observability solutions.

5. Utilize AI

An AI-driven solution makes observability truly actionable by addressing cloud complexity challenges. It helps interpret vast data streams from multiple sources at increasing velocities. With a single source of truth, teams can quickly and accurately pinpoint root causes before performance degrades or accelerate recovery if a failure occurs.

Frequently Asked Questions on Observability – FAQs

What is observability in simple terms?

This refers to the fact that it is possible to understand exactly what is happening within a system such as an app or a website by looking at the data being generated.

Why is observability important for IT teams?

It enables them detect and fix problems instantly before users are affected, by giving them a holistic view of all moving parts and technologies.

How is observability different from traditional monitoring?

Monitoring looks out for issues that are known while observability allows you to investigate and recognize unforeseen difficulties that were not expected.

What are the key types of data used for observability?

Logs (event records), metrics (measurements over time), traces (a route through a request) and user experience data.

What are some challenges with observability in modern cloud environments?

These include too much information from multiple directions, manual configuration work involved, understanding connections between distributed components, and aligning information across teams/tools.

How can AI and automation help with observability?

AI can analyze huge data volumes to automatically expose problems and causes. Automation detects components and captures data without interruption.

Besides IT operations, how else is observability valuable?

It provides business insights by connecting technical data to customer experiences, conversions, and objectives – helping prioritize decisions.