How to load data from Gong to Weaviate

Learn how to use Airbyte to synchronize your Gong 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 Gong 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 Gong 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 Gong 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.

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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

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: Extract Data from Gong

Begin by exporting your required data from Gong. Access the Gong platform, and use its built-in export functionality to download the data as CSV or JSON files. Ensure that you have the necessary permissions to perform data exports and that you select the specific data fields you need for your Weaviate instance.

Step 2: Prepare Data for Weaviate

Once you have the data exported, examine the file format (CSV or JSON) and ensure it meets Weaviate’s data ingestion requirements. If necessary, clean and preprocess the data to match the schema you plan to use in Weaviate, including data types and structures.

Step 3: Define Schema in Weaviate

Access your Weaviate instance and define the schema that will hold your Gong data. Use the Weaviate Console or API to create classes and properties that represent your data structure. Ensure the schema reflects the cleaned and preprocessed data format you prepared earlier.

Step 4: Transform Data to Match Schema

Transform your CSV or JSON data to match the schema defined in Weaviate. This may involve scripting a transformation process using a programming language like Python to map fields from your Gong data to the appropriate classes and properties in Weaviate.

Step 5: Write a Data Import Script

Develop a script to automate the data import process into Weaviate. Using a language such as Python, utilize Weaviate's RESTful API to programmatically send HTTP POST requests to import your transformed data into the appropriate classes. Ensure your script handles authentication and error checking.

Step 6: Execute Data Import

Run your data import script to move the data from your local environment into Weaviate. Monitor the execution to ensure that all data is correctly imported and that there are no errors. If issues arise, debug and adjust your script or data transformation process as needed.

Step 7: Validate Data in Weaviate

After importing the data, validate that it has been correctly ingested into Weaviate. Use Weaviate’s search and query capabilities to confirm that the data is complete and accurate. Check for consistency with your original Gong data and perform additional tests to validate data integrity.

By following these steps, you can move data from Gong to Weaviate without relying on third-party connectors or integrations, ensuring a streamlined and controlled data transfer process.