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