How to load data from BigQuery to BigQuery

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

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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 BigQuery 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 BigQuery 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 BigQuery 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’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.

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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: Identify the Source and Destination Datasets

Begin by identifying the source dataset and table from where you will be moving the data, as well as the destination dataset and table where the data needs to be moved. Ensure you have access permissions to both datasets.

Access the Google Cloud Console at https://console.cloud.google.com. Navigate to the BigQuery section by selecting "BigQuery" from the navigation menu.

Write a SQL query that selects the data you want to move from the source table. For example:
```sql
SELECT FROM `source_project.source_dataset.source_table`
```
This query will select all columns and rows from the specified source table.

Utilize the `CREATE TABLE AS SELECT` (CTAS) statement to directly transfer the data to the destination table. In the SQL workspace, execute a query similar to:
```sql
CREATE TABLE `destination_project.destination_dataset.destination_table` AS
SELECT FROM `source_project.source_dataset.source_table`
```
This command creates a new table in the destination dataset and populates it with data from the source table.

Execute the query and monitor the progress in the console. You can view the job details under the "Query History" section to ensure it completes successfully. This will confirm that the data has been moved correctly.

Once the job is complete, verify the integrity of the transferred data. Run a simple count or checksum query on both the source and destination tables to ensure the data matches:
```sql
SELECT COUNT() FROM `destination_project.destination_dataset.destination_table`
```
Compare this with the count from the source table.

If necessary, perform any cleanup tasks such as removing temporary tables or optimizing the destination table for performance. You might want to partition or cluster the destination table based on your access patterns for optimal query performance.

By following these steps, you can successfully move data between BigQuery datasets internally without relying on third-party tools.