How to load data from Unleash to BigQuery

Learn how to use Airbyte to synchronize your Unleash data into BigQuery within minutes.

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Building your pipeline or Using Airbyte

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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 Unleash connector in Airbyte

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

Set up BigQuery for your extracted Unleash 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 Unleash to BigQuery 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.
Try Airbyte CloudManual method (the hard way)

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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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.

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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.

Try Airbyte CloudManual method (the hard way)

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.

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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: Access Unleash Data

First, log in to your Unleash account and navigate to the section where your data is stored. Identify the specific datasets or reports you want to move to BigQuery. Ensure you have the necessary permissions to export the data.

Use the export functionality within Unleash to download your data in a CSV or JSON format. Most platforms allow exporting data through their user interface, often found under options like "Download," "Export," or "Save As." Make sure to save the exported files securely on your local machine.

Review the exported data files and clean them if necessary. This might involve removing unnecessary columns, fixing data types, and ensuring the data is consistent and properly formatted. This step is crucial to avoid errors during the import process into BigQuery.

If you haven't already, create a Google Cloud account and set up a new project in the Google Cloud Console. Configure billing settings and enable the BigQuery API under the "APIs & Services" section to allow you to interact with BigQuery.

In the BigQuery console, create a new dataset to store your imported data. A dataset is a logical grouping that holds tables. Assign a unique name and set any required data access permissions to ensure data security.

Use the BigQuery web UI to upload your CSV or JSON files. Go to the BigQuery console, select your dataset, and click "Create Table." Choose "Upload" as the source, and then select your file and specify the file format. Map the columns in your file to BigQuery fields and set the appropriate data types. Follow the wizard to complete the upload process.

Once the data is uploaded, run a few queries in BigQuery to ensure the data has been imported correctly. Check for discrepancies or errors by comparing a few records with the original data in Unleash. This ensures that the data has been transferred accurately and is ready for analysis.

By following these steps, you can successfully move your data from Unleash to BigQuery without relying on third-party connectors or integrations.