How to load data from Elasticsearch to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Elasticsearch data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Elasticsearch connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Elasticsearch data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Elasticsearch to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Export Data from Elasticsearch

Begin by exporting the data from your Elasticsearch cluster. Use the Elasticsearch Scroll API to efficiently handle large datasets. The Scroll API is designed to retrieve large amounts of data in a paginated manner. Write a script to iterate through the data and store it in a format that can be easily transferred, such as JSON or CSV files.

Prepare a temporary storage location to hold the exported Elasticsearch data. This could be a cloud storage service like AWS S3, Azure Blob Storage, or a local file system that Databricks can access. Ensure that this location is configured with the appropriate permissions for both reading and writing data.

Transfer the exported data from your local environment (or wherever the data is currently stored) to the chosen temporary storage location. Use secure transfer protocols like SCP, SFTP, or the native upload tools provided by your cloud storage service to ensure data integrity during the transfer.

Log into your Databricks account and create a new cluster if one does not already exist. Configure the cluster with the necessary compute resources based on the size of the data and the complexity of the transformations you plan to perform.

In Databricks, use Spark to access the data stored in your temporary storage location. This can be done using the appropriate Spark APIs, such as `spark.read.csv()` for CSV files or `spark.read.json()` for JSON files. Configure the access credentials and paths to correctly point to the files stored in the temporary location.

Once the data is loaded into Databricks, perform any necessary transformations or cleansing operations using PySpark or Spark SQL. This might include operations such as filtering, joining, or aggregating data to prepare it for analysis.

Finally, write the transformed data into your Databricks Lakehouse. Use Spark’s `write` methods to save the data in a format optimized for analysis, such as Delta Lake format. Specify the desired partitioning and bucketing to optimize query performance, and ensure data is stored in the correct location within your Lakehouse architecture.