Observability:
Use-Cases and Examples

Observability is the practice of measuring the state of a system based on external output using metrics, logs, and traces. It is the evolution of monitoring, as it helps you learn about your IT environment’s critical aspects and discover potential problems before they arise.

Observability transforms the speed, cost, and efficiency of your machines. While Observability benefits most enterprises, the amount of observability employed should be determined by your business needs.

What is Observability Blog

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What Problem Does Observability Solve?

Your enterprise’s business goals include strong security, high-service stability, monitoring tools, and increased customer experience. To understand how you’re meeting those goals, you must collect and analyze data correlated with your desired outcomes.

You can start by collecting and analyzing the three main components of Observability – metrics, logs, and traces. For example, organizations collect data from different data sources and analyze it with the proper observability tools and in the right format to have a comprehensive view of how your environment is performing.

Observability Use Cases

Platform
Security

The observability platform monitors the software for any abnormal activity and network breaches. The engineers are then alerted and can mitigate the issues while adhering to data governance principles.

Manage Data in
Real Time

Data in logging systems are usually unstructured or noisy. The operations team can implement observability to process data through the pipeline, convert it in related formats and eliminate the noise from it.

Monitor Application Performance

Observability gives engineers control over data to identify unknowns in the system. It allows them to monitor the underlying logs, metrics, and traces for the environment to track the enterprise's digital transformation.

How to Optimize Observability

Observability helps organizations understand and optimize their IT systems. To use observability effectively, you should understand how your IT systems impact your goals, make a list of questions about how your systems are operating, translate those questions into things you can measure, and decide what measurements are acceptable. You can collect data for observability using servers, log scrapers or forwarders, and agents (software that collects metrics from endpoints). This will help you understand what is happening within your environment.

3 Reasons to Use an Observability Pipeline Like Stream

The fastest way to get started with Cribl Stream is to sign-up at Cribl.Cloud. You can process up to 1 TB of throughput per day at no cost. Sign-up and start using Stream within a few minutes.

Route Your Data to Multiple Tools and Destinations

With an observability pipeline, you can take data from any source and route it to any tool. Put data where it has the most value. Route data to the best tool for the job — or all the tools for the job.

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Reduce Data With Little Analytical Value to Control Costs

An observability pipeline can help you reduce less-valuable data before you pay to analyze or store it. This process can help you dramatically slash costs, eliminate null fields, remove duplicate data, and drop fields you’ll never analyze. Using an observability pipeline means you keep all the data you need and only pay to analyze and store what’s important to you now.

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Transform Data Into Any Format Without Adding New Agents

Take the data you have and format it for any destination, without having to add new agents. By transforming the data you already have, and sending it to the tools your teams use, this increases flexibility without incurring the cost and effort of recollecting and storing the same data multiple times in different formats.

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Observability Blogs

White Papers & ebooks

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