There are several different types of data transformation tools that can be used with Elasticsearch. Here are some of the most common ones:
1. Logstash Filters: Logstash provides a wide range of filters for different types of data transformation, such as the grok filter for parsing unstructured text data, the dissect filter for extracting fields from structured data, and the geoip filter for enriching data with geographic information.
2. Elasticsearch Pipelines: Pipelines are a powerful way to apply a series of filters to incoming data in a specific order. Each filter can modify the data in some way, and the resulting data is then indexed into Elasticsearch.
3. Elasticsearch Ingest Node: Elasticsearch Ingest Node is a feature that allows for data transformation to be performed within Elasticsearch. Ingest Node provides a set of processors that can be used to modify the content of fields or perform calculations on the data as it is indexed.
4. Elasticsearch Scripting: Elasticsearch provides support for scripting languages such as JavaScript and Python. Scripts can be used to modify the content of fields or perform calculations on the data as it is indexed.
Overall, there are many different tools that can be used for data transformation in Elasticsearch, depending on the specific needs of the application. By choosing the right tool for the job, you can ensure that data is efficiently and effectively transformed before being indexed into Elasticsearch and made available for search and analysis.