What is observability?

Observability is a concept from control theory that has become widely used in software engineering and systems monitoring. At its core, observability is the ability to understand what is happening inside a system based on the data it produces—usually logs, metrics, and traces.

Observability is about answering questions like:

• Why is this system slow?
• What caused this error?
• What’s the impact of this change?

Instead of just alerting when something is wrong, an observable system helps figure out why it's wrong and how to fix it.

Observability is made of three key components: metrics, logs and traces.Metrics are numerical data that reflect the performance or state of a system. Examples of metrics include CPU usage, memory consumption, or request or error rates. Logs are text-based records of events and messages from applications. They are typically used to pinpoint the exact cause of problems. Traces track the journey of a single request through multiple services, and are essential tools for understanding complex distributed systems and microservices.

Observability is a critical component of modern software development, and is increasingly a differentiating factor for corporate success. An observable software development process allows devs and ops to debug issues faster, supports, proactive detection of potential failures, improves reliability and performance, and supports high uptime which maintains customer trust and ultimately satisfaction.

How does observability work?

Observability works by collecting and analyzing data from your system to understand its internal state, especially when something goes wrong.

Software development teams generally tackle observability through four main steps: instrumentation, data collection, correlation and analysis and visualization and alerting.

Instrumentation is what allows the code or infrastructure to actually be able to communicate what’s going on; this happens by adding in logs (to find out what happened), metrics (to determine how many, how long, and how often), and traces (to track where the requests went and what might have slowed them down). 

Data collection allows all the information from the instrumentation to be ingested and stored in real-time.

Correlation & analysis is where the observability detection begins, linking logs, metrics and traces together so teams can ask and eventually answer key questions.

And finally, visualization and alerting are the dashboards, charts and alert rules built by the team to make it easier to see trends and spikes, set alerts, and drill down from high-level metrics to logs and traces. What are the benefits of observability?

What are the benefits of observability?

In today’s world of complex distributed systems, observability is invaluable. Here are the main benefits:

Issues are detected - and resolved - much more quickly.

Observability means teams see issues often even before users report them, *and* then they can quickly find the cause. No more guessing, or randomly poking through logs. See it, resolve it, move on.

Easily understand how systems behave in the real world.

Deep system understanding isn’t just a “nice to have” with observability - it’s what naturally happens. Engineers now have a clear view of dependencies, performance bottlenecks and usage patterns, and can see how a system behaves under real world conditions.

Reliability and performance improve dramatically.

With continuous monitoring it’s a simple matter to improve uptime, latency and overall system health. Now it’s easy to proactively fix issues before they escalate.

Deploy more rapidly than ever before.

Observability is CI/CD’s BFF. Now teams can look at a new release and quickly see if errors have gone up or performance has changed, all of which means faster and safer deployments.

Drastically reduce MTTR.

Because observability serves up the visibility and the context, it’s quick to find and fix a problem, meaning MTTR will plummet. 

Expand business side buy-in.

Observability brings knowledge and understanding to the entire organization, not just engineering. Product teams can now see usage patterns while business teams can understand user behavior and system limitations.

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What is monitoring?

Monitoring is the practice of collecting, analyzing, and visualizing pre-defined metrics or logs to ensure systems are running as expected.

It’s focused on detecting known issues or threshold breaches and alerting when something goes wrong.

Monitoring is generally a four-step process: define what to watch (CPU usage, memory, request counts, error rates), use thresholds, use tools to collect and visualize the data, and trigger alerts when the thresholds are crossed.

When teams approach monitoring, they’re looking at predefined targets, meaning they’re monitoring things they already know are important. Also, monitoring is by its very nature static (because it’s predefined) meaning the unexpected might be missed. And finally monitoring is very focused on alerts; teams reliant on monitoring are waiting to be notified when something breaks.

What are the core types of monitoring?

Monitoring can be broken down into a few core types, each focusing on a different part of the system or infrastructure. Here's a breakdown of the seven main types of monitoring seen in modern systems:

Infrastructure monitoring tracks the health and performance of servers, VMs, containers, networks, etc. Application Performance Monitoring (APM) measures how well an application is performing from the inside, and can be used to ID slow database queries or memory leaks. Network monitoring keeps an eye on data flow across your network, including connectivity and performance. Security monitoring watches for security threats, vulnerabilities, or malicious activity in real time. Synthetic monitoring simulates user behavior to test system availability and performance. Log monitoring collects and scans logs for patterns, errors, or anomalies. And database monitoring tracks the health and queries of your databases.

What are the benefits of monitoring?

While observability helps to understand why something broke, monitoring helps to catch it in the first place. Monitoring is a system’s early warning system.

There are a number of benefits of monitoring.

First, teams can detect issues early, before they become bigger problems, because alerts have been triggered to show something has deviated from normal. And early detection and clear visibility into key metrics can lead to faster troubleshooting. The less time teams spend guessing the more time they have to get problems resolved.

Monitoring generates a lot of data, which teams can use for performance optimization, and that can then lead to improved reliability and uptime. The more stable and resilient a system is, the more it can meet SLAs (service-level-agreements) and avoid outages.

It’s also possible to use monitoring to ensure smooth deployments, because teams can see what’s happening in real time. 

On the opposite end of the spectrum, all of the monitoring data is ripe for historical analysis, meaning teams can ID long-term trends, forecast scaling needs and compare performance rates before and after changes. 

Monitoring also allows teams to be proactive, using alerts and automation to auto-restart services, scale resources and notify the right teams. And finally, the shared dashboards, data and alerts keeps the entire team - not just the engineers - on the same page, supporting a culture of transparency and accountability.

In short, monitoring is your first line of defense—it helps you see that something’s going wrong, so you can take action before your users feel the pain.
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What is telemetry data?

Telemetry data is the automatic, remote collection of data from your systems, applications, or infrastructure, sent to a central location for analysis and monitoring. Telemetry is the foundation of both monitoring and observability. Without telemetry monitoring tools have nothing to watch and observability tools have nothing to analyze.

Think of it as the "signals" your systems send out to let you know what’s going on inside—without you needing to constantly check or be on the system. Telemetry comes from the Greek: Tele meaning “distant” and Metron meaning “measure” so “measuring from afar.”

Telemetry data includes the raw data points that power monitoring and observability tools. There are three basic types of telemetry data - metrics, logs and traces. Metrics are quantitative data points that can be measured over time and are normally numeric and time-stamped. Logs are text-based records of events, errors, or status updates and are often unstructured or semi-structured. Traces request lifecycle tracking across services or systems, and show were time was spent, what services were called and what failed.

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What is the difference between observability and monitoring?

Monitoring detects known issues while observability investigates unknown issues. To use a non-technical analogy, monitoring is like a smoke detector which alerts where there is smoke, but a fire investigation is what will actually explain why the fire happened, and that’s observability.

Monitoring is a subset of observability, and they truly are *better together.* Observability is not a replacement, but an evolution—it includes monitoring, and goes much further.

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What are the similarities between observability and monitoring?

While observability and monitoring serve different purposes, they work closely together and share several key similarities.

Both observability and monitoring improve system reliability by helping detect issues early and preventing downtime. Both rely on telemetry data and need it to understand system behavior. Both monitoring and observability support alerting and troubleshooting; they are used to detect problems, trigger alarms, and begin the process of root cause analysis. Many modern monitoring and observability tools overlap, providing dashboards that monitor *and* provide deeper observability, and both aim to make systems more transparent. And modern DevOps and SRE teams wouldn’t be able to function without both monitoring and observability.

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How does Mezmo help with monitoring and observability?

Mezmo is a comprehensive observability and monitoring platform designed to help engineering, DevOps, and SRE teams manage telemetry data efficiently. It provides tools to collect, process, and analyze logs, metrics, traces, and events, enabling faster incident response and optimized data usage.​

By leveraging Mezmo's capabilities, organizations can enhance their monitoring and observability practices, leading to improved system reliability, faster issue resolution, and optimized operational costs.​

Mezmo (formerly LogDNA) is a telemetry data pipeline and observability platform designed to help organizations efficiently manage, analyze, and route their telemetry data for enhanced monitoring and observability. Here's how Mezmo supports these functions:​

Centralized Telemetry Data Management

Mezmo provides a unified platform to ingest telemetry data—such as logs, metrics, and traces—from various sources. This centralized approach allows DevOps teams to collect all observability data in one place, facilitating easier analysis and routing to appropriate destinations like SIEM solutions or log aggregators. 

Data Transformation and Enrichment

The platform enables transformation and enrichment of telemetry data, ensuring that the data is in the right format and context for analysis. This process enhances the quality and usability of the data, making it more actionable for monitoring and observability purposes.

Cost Optimization through Log Volume Reduction

Mezmo helps organizations reduce observability costs by eliminating unnecessary data, thereby decreasing log volumes.This optimization ensures that only relevant data is stored and analyzed, leading to significant cost savings. 

Enhanced Data Accessibility and Visualization

The platform offers improved data accessibility and visualization tools, allowing teams to quickly onboard new data sources and migrate to new observability platforms. This flexibility supports better decision-making and faster incident response.

Integration with Existing Tools

Mezmo is designed to integrate seamlessly with existing monitoring and observability tools. It can route data to various platforms, enabling organizations to leverage their current toolsets while enhancing their observability capabilities. 

What are the benefits of the Mezmo Platform?

The Mezmo platform (formerly LogDNA) offers a modern, powerful way to handle telemetry data across complex environments. It’s designed to streamline observability and monitoring workflows for engineers, DevOps, and SRE teams.

The Mezmo platform helps teams detect and fix issues faster, reduce telemetry storage and processing costs, automate telemetry pipelines and workflows, visualize and analyze system health in real time, and stay secure and compliant.

Here are the key benefits of Mezmo:

Two examples of how Mezmo has helped modernize observability

​Sysdig, a company specializing in cloud-native security and monitoring solutions, faced challenges in efficiently accessing and utilizing their log data, which is crucial for monitoring and securing cloud-native environments. By integrating Mezmo's telemetry pipeline, Sysdig achieved an 80% improvement in the time it takes to access and use log data. This enhancement significantly accelerated their incident response and troubleshooting capabilities, leading to improved system reliability and performance. 

Mezmo has also had a partnership with IBM since 2018 when it became the sole logging provider for IBM Cloud, supporting thousands of internal teams and enterprises. Deployed across eight multi-zone regions globally, Mezmo's solution manages petabytes of logs, providing centralized logging for IBM Cloud services, including IBM Watson and the Weather Company. 

How Mezmo has helped Site Reliability Engineers (SREs)

To understand the impact of observability and monitoring on SREs, look no further than Mezmo's own platform team, which utilized the company's telemetry pipeline to enhance their metrics handling processes, leading to improved observability and system reliability.​

The Mezmo platform team faced challenges in efficiently managing and processing a vast array of metrics essential for maintaining system performance and reliability. By implementing Mezmo's telemetry pipeline, they achieved:

Streamlined Metric Ingestion: The team could ingest metrics from diverse sources, ensuring comprehensive visibility into system operations.​

Efficient Data Transformation: Utilizing the pipeline's capabilities, they transformed and enriched metric data, making it more actionable and easier to analyze.​

Optimized Data Routing: The pipeline allowed for intelligent routing of metrics to appropriate destinations, such as monitoring dashboards or alerting systems, facilitating quicker response times to potential issues.​

This implementation not only improved the team's ability to monitor system health but also reduced the overhead associated with managing complex metric data flows. ​

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