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Data management in Elasticsearch

Serverless

Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Serverless interfaces and procedures might differ from classic Elastic Stack deployments.

Stack

The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.

This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

Whether you're looking to build a fast and relevant search solution, monitor business-critical applications and infrastructure, monitor endpoint security data, or one of the many other use cases Elastic supports, you need to understand how to ingest and manage data stored in Elasticsearch.

The fundamental unit of storage in Elasticsearch, the index, is a collection of documents uniquely identified by a name or an alias. These documents go through a process called mapping, which defines how Elasticsearch stores and indexes a document and the fields it contains, and a process called text analysis in which Elasticsearch converts unstructured text into a structured format that's optimized for search.

Learn more in The Elasticsearch data store.

Before you can start searching, visualizing, and pulling actionable insights from Elastic, you have to get your data into Elasticsearch. Elastic offers a wide range of tools and methods for getting data into Elasticsearch. The best approach depends on the kind of data you're ingesting and your specific use case.

Learn more in Ingestion.

After you've added data to Elasticsearch, you need to manage it over time. For example, you can specify that data be deleted after a retention period or store data in multiple tiers with different performance characteristics.

Strategies for managing data depend on the type of data and how you're using it. For example, with a collection of items you want to search, like a catalog of products, the value of the content remains relatively constant over time so you want to be able to retrieve items quickly regardless of how old they are. Whereas with a stream of continuously-generated timestamped data, such as log entries, the data keeps accumulating over time, so you need strategies for balancing the value of the data against the cost of storing it.

Learn more in Data lifecycle.

If you move to new infrastructure, there are several options for moving existing data between Elasticsearch clusters.

Learn more in Migrate your Elasticsearch data.