Observability platforms are good at providing a unified view of your log data for use cases like application monitoring and root cause analysis. But what happens to that data once it’s no longer needed for observability?
More often than not, log data is thrown away after a fairly short period of time, with retention windows of 30 days or less fairly typical. Observability platforms aren’t designed to retain data long term for other use cases like data science and training machine learning models, nor do they tend to integrate well with platforms designed for business analytics.
Forward-thinking organizations are taking a fresh look at their log data and realizing that it has tremendous value for use cases beyond observability. But to realize this value, enterprises need to retain the data long-term and make it available to teams and tools outside of their observability platforms.
The problem: observability platforms simply aren’t designed to do that.
Our latest infographic explains the importance of federating log data for business intelligence, data science, machine learning, and other use cases. And it’s also a resource designed to help you find the right log storage solution for data federation so that you can make log data your competitive edge.
Next Steps
- Learn about using the Hydrolix Connector for Apache Spark (Databricks), including how the connector works and how Hydrolix performs against Delta Lake.
- Read about the latest updates to the Hydrolix Connector for Apache Spark
- Read about the Hydrolix Connector for Apache Spark (AWS EMR).
- Read about the Hydrolix Connector for Apache Spark (Microsoft Fabric).
- Check out the Hydrolix Connector for Apache Spark documentation.
- Contact Hydrolix about a proof of concept or demo.
