How to load data from Apify Dataset to Weaviate

Learn how to use Airbyte to synchronize your Apify Dataset data into Weaviate within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Apify Dataset 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 Apify Dataset 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 Apify Dataset 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: Understand Apify Output

First, familiarize yourself with the structure of the data exported from Apify. Typically, Apify results are available in JSON format. Access your Apify account, navigate to the specific actor run, and download the dataset in JSON format. This will be your source file for migration.

Ensure you have a running Weaviate instance, either locally or on a cloud service. Follow Weaviate's official documentation to install and configure it. You'll need to know the endpoint URL of your Weaviate instance to interact with it using HTTP requests.

Before importing data, define the schema in Weaviate according to the structure of your Apify data. Use Weaviate's schema endpoint to create classes and properties that match the JSON keys from your Apify dataset. Ensure the data types align appropriately (e.g., strings, integers, floats).

Convert your JSON data into a format suitable for Weaviate's import API. This involves mapping JSON keys and values to the schema defined in Weaviate. You might need to write a script in a language like Python to transform the data, ensuring it adheres to Weaviate's expected format.

If your Weaviate instance requires authentication, set up the necessary credentials. This might include an API key or token. Ensure your script or tool used for importing data includes these credentials in the headers of HTTP requests to authenticate each transaction.

Use a programming language such as Python with an HTTP client library (e.g., `requests`) to send your prepared data to Weaviate. For each item in your dataset, make a POST request to the Weaviate `/objects` endpoint. Handle any response errors to ensure that each piece of data is successfully imported.

After the import, verify that the data in Weaviate matches your original Apify dataset. Use Weaviate's query capabilities to retrieve a sample of the imported data and compare it against the source JSON. Check for completeness and accuracy to ensure the migration was successful.

By following these steps, you can effectively move data from Apify to Weaviate without relying on third-party connectors or integrations.