> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://mezmo.ferndocs.com/elasticsearch-destination/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://mezmo.ferndocs.com/_mcp/server. # ElasticSearch ## [Description](https://docs.mezmo.com/docs/elasticsearch-pipeline-destination#description) Typically you would use ElasticSearch to store and analyze large amounts of data which are of different structures and formats. An ElasticSearch cluster is composed of **Clusters**, **Indices**, **Nodes**, and **Shards** that help organize and manage how your data is stored. The data can then be efficiently and powerfully searched and analyzed. ElasticSearch is usually used as a Pipeline destination when your log data needs to be indexed for searching. By setting up a Pipeline for your ElasticSearch data, you can use Pipeline Processors like [Dedupe](https://docs.mezmo.com/2.8/telemetry-pipelines/dedupe-processor) and [Remove Fields](https://docs.mezmo.com/2.8/telemetry-pipelines/drop-fields-processor) to clean data or drop it if it’s not valuable before sending it to ElasticSearch. ## [Configuration Options](https://docs.mezmo.com/docs/elasticsearch-pipeline-destination#configuration-options) | Option | Description | | -------------------------- | ------------------------------------------------------------------------------------------------------ | | End-to-End Acknowledgement | Enable this option to receive verification that log data is being received by ElasticSearch. | | Compression | Compression type to apply to your log data. | | Strategy | The authentication strategy of your ElasticSearch destination, with options for **Basic** and **AWS**. | | Endpoints | The full URL(s) of the ElasticSearch destination(s). | | Pipeline | The name of the ElasticSearch ingest pipeline to use. | | Custom Index | The index name or pattern of the ElasticSearch destination. |