Index lifecycle management for time series data in Elasticsearch

Yaron Parasol

Matthew Adams

Principal Solutions Architect

Elastic

Many Elasticsearch users index time series data such as logs, metrics, and telemetry data. As this data ages, it’s necessary to ensure that it’s being stored in the most cost-effective way. In this webinar, we’ll cover how to use the new index lifecycle management (ILM) policy feature — which became generally available in the 6.7 release of the Elastic Stack — to manage time series data. We’ll show you how ILM policies take advantage of other data management features in Elasticsearch and do a demo of ILM with data shipped from Beats.

Highlights:

• Best practices for managing aging data using ILM
• How ILM works with Beats
• Phases and actions available in ILM
• How to manage ILM policies from Kibana
• How ILM works with frozen indices
• How ILM works in Elastic Cloud and Elastic Cloud Enterprise

Additional resources

• Blog: Implementing a Hot-Warm-Cold Architecture with Index Lifecycle Management
• Blog: Creating frozen indices with the Elasticsearch Freeze index API
• Blog: Deploying a Hot-Warm Logging Cluster on the Elasticsearch Service
• Documentation: Managing the index lifecycle