How to load data from Elasticsearch to Weaviate

Learn how to use Airbyte to synchronize your Elasticsearch data into Weaviate 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 Weaviate 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 Weaviate 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: Prepare Elasticsearch Data Export

First, you need to export the data from Elasticsearch. You can do this by using Elasticsearch's built-in tools like the `_search` API. You will need to create a script that queries Elasticsearch for all the documents you wish to transfer. Ensure to paginate through the data if you have a large dataset, using the `scroll` API to handle large datasets efficiently.

Once you have queried the data from Elasticsearch, transform the data into a JSON format. Elasticsearch typically returns data in JSON, but you may need to adjust the structure or keep only the necessary fields that you want to transfer to Weaviate. This might involve removing metadata or reformatting fields to match Weaviate’s schema requirements.

Before importing data into Weaviate, you need to define the schemas that correspond to your data structure. This involves setting up the classes, properties, and types in Weaviate that correspond to the data fields from Elasticsearch. This can be done through Weaviate’s RESTful API by sending a POST request to the `/v1/schema` endpoint with your schema configuration.

Ensure you have access to the Weaviate instance and authenticate using an API key or another authentication method configured on your Weaviate server. This step is critical as it allows you to perform data operations on your Weaviate instance securely.

With your data formatted and schema set, you can begin loading the data into Weaviate. Use Weaviate’s RESTful API to POST the data to the `/v1/objects` endpoint. This step involves writing a script that iterates over your JSON data and sends each document to Weaviate, mapping each document's fields to the schema you defined.

After importing the data, verify that the data in Weaviate matches the data in Elasticsearch. You can do this by performing queries in Weaviate and cross-referencing the results with the original data set. Check for data completeness, accuracy, and ensure that the document relationships are maintained where necessary.

Once data is successfully transferred, consider optimizing Weaviate for performance based on your use case. This might include configuring vector search settings, adjusting resource allocations, or tuning schema configurations. It's essential to ensure that Weaviate is set up to handle search queries efficiently with your newly imported data.
By following these steps, you can effectively move data from Elasticsearch to Weaviate without relying on third-party connectors or integrations.