Data transformation in Elasticsearch refers to the process of changing the format, structure, or content of data as it is indexed into the Elasticsearch cluster. Data transformation can be performed using various techniques, such as filters, pipelines, and scripts.
Filters are the most common technique used for data transformation in Elasticsearch. Filters are applied to incoming data before it is indexed to modify the structure or content of the data. Elasticsearch 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.
Pipelines are another powerful technique for data transformation in Elasticsearch. Pipelines are a way to apply a series of filters to incoming data in a specific order. Each filter in the pipeline can modify the data in some way, and the resulting data is then indexed into Elasticsearch. Pipelines can be used to perform complex data transformation tasks, such as parsing and enriching log data, or converting data from one format to another.
Scripts are another technique for data transformation in Elasticsearch. Scripts can be used to modify the content of fields or perform calculations on the data as it is indexed. Elasticsearch supports several scripting languages, such as JavaScript and Python, and provides a wide range of built-in functions and operators for data manipulation.
Overall, data transformation is an important part of indexing data into Elasticsearch. By applying filters, pipelines, or scripts to incoming data, Elasticsearch can modify the structure or content of the data to make it more searchable and useful for analysis.