Observability is a growing practice in the world of software development and operations. Observability gives you the opportunity to learn about the important aspects of your environment without knowing in advance the questions you need to ask. Put more simply, it seeks to answer how much you can understand about a system by looking at it from the outside.

An Observability Pipeline is the connective tissue between all of the data and tools you need to view and analyze data across your infrastructure. The pipeline consolidates the collection of data, transforms it to the right format, and routes it to the right tool.

In order to ask questions of the data, it has to be structured in a way that the analytics tools your organization uses can understand. Unfortunately, many data sources have unique structures that aren’t easily readable by all analytics tools, and some data sources aren’t structured at all. Some of the tools your organization uses to review and analyze data may expect the data to have already been written to log files in a particular format, known as schema-on-write, and some tools involve an indexing step to process the data into the required format as it arrives, known as schema-on-read.

To take advantage of the wide variety of options possible for structure, content, routing, and storage of your enterprise data, an observability pipeline allows you to ingest data and get value from that data in any format, from any source, and then direct it to any destination — without breaking the bank. An observability pipeline can result in better performance and reduced infrastructure costs.

As your goals evolve, you have the freedom to make new choices, including new tools and destinations as well as new data formats. The right observability pipeline helps you get the data you want, in the formats you need, to wherever you want to go.

View this pre-recorded webinar to learn more about best practices for creating and implementing an Observability Pipeline.

In this video you’ll learn:

• How Cribl Stream helps you create an Observability Pipeline
• Techniques for processing observability streams
• How to route data from one collector to multiple destinations
• How to process log data in batch and “replay” logs from long-term storage locations
• How to reduce log analytics costs and infrastructure
• Transform data into any format without adding new agents
• Replay – collect data from cheap storage and “replay” to an analytics tool later as needed
• Enrich data with third-party sources like Geo-IP and Known Threats databases to give deeper context
• Collect data from REST APIs for more comprehensive analysis