How to load data from Fauna to Weaviate

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

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines

Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Fauna 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 Fauna 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 Fauna 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.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman headshot

Chase Zieman

Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Understand Data Structures in Fauna and Weaviate

Before migrating data, familiarize yourself with the data structures in both Fauna and Weaviate. Fauna uses a document-based model, whereas Weaviate is a vector search engine that stores data as objects. Identify how your data in Fauna will map to objects in Weaviate.

Step 2: Export Data from Fauna

Use Fauna's FQL (Fauna Query Language) to query and extract the data you need. You can run queries via the Fauna Dashboard or through your application’s backend. Export the data in a suitable format like JSON, which can easily be handled by scripts or programs for further processing.

Step 3: Prepare Data for Transformation

Once you have your data exported from Fauna, review the structure and clean it as needed. Ensure that the data is complete and consistent, and decide on any transformations required to meet Weaviate's schema requirements.

Step 4: Design Weaviate Schema

Before importing data into Weaviate, define the schema that will represent the data. This includes defining classes and properties that match the data structure you’ve exported from Fauna. Use Weaviate's schema capabilities to create classes that align with your data's structure.

Step 5: Transform Data into Weaviate Format

Write a script (in Python, JavaScript, etc.) to transform your exported JSON data into the format required by Weaviate. This involves mapping Fauna data fields to Weaviate object properties, ensuring compliance with the schema designed in the previous step.

Step 6: Write Data to Weaviate

Using Weaviate’s RESTful API, send HTTP POST requests to insert the transformed data into Weaviate. Ensure you handle authentication and batch requests where possible to efficiently load large datasets. Test with a small subset of data before full migration to detect any issues early.

Step 7: Verify Data Integrity

After importing, verify that all data has been accurately transferred and is accessible in Weaviate. Perform queries to check data consistency and integrity. Compare results with your original data in Fauna to ensure the migration was successful, and document any discrepancies for troubleshooting.

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