Open-source data movement to
Open-source ETL from
BigQuery
to any destination
BigQuery is a scalable, cost-effective multi-cloud data warehouse for business agility, enabling fast SQL queries with Google's powerful infrastructure. Airbyte enables you to sync from any data source to BigQuery, in minutes.
BigQuery is a scalable, cost-effective multi-cloud data warehouse for business agility, enabling fast SQL queries with Google's powerful infrastructure. Airbyte allows you to extract and sync data from BigQuery to any data warehouse, lake, database, or other destination within minutes.
Start syncing data from any source to BigQuery in three easy steps
Start leveraging your BigQuery data in three easy steps
Set up a source connector to extract data from Airbyte
Setup a BigQuery connector in Airbyte
This can be any API tool, cloud data warehouse, database, data lake, file, or many other source types.
Set up BigQuery as the destination connector
Set up a destination for your extracted BigQuery data
Connect to BigQuery or one of 50+ Airbyte data destinations through simple account authentication.
Configure the connection in Airbyte
Select the data you want to extract, the sync frequency, and where in BigQuery you want that data to be loaded.
Airbyte's
Open Data Movement Platform
Modernize your data infrastructure with Airbyte's high speed data replication. Move large volumes of data with best-in-class CDC methods and replicate large databases within minutes.
Why Airbyte?
Airbyte is the only unified data movement platform built on the open standard. It is uniquely positioned in terms of data sovereignty, connector extensibility, and support for AI workflows.
Syncing data from BigQuery is only one of your 1,000 future data pipeline needs. Leverage the largest Marketplace of 400+ pre-built and 10,000+ custom structured and unstructured connectors. Join 2,000 + data engineers who built 7,000+ custom connectors in minutes with low-code/no-code Connector Builder or AI Assistant.
Create context for AI agents by leveraging Airbyte's 600+ connectors. Airbyte's pipelines transfer structured and unstructured data together for metadata preservation. With support for flexible destinations such as Iceberg, Airbyte is the ideal data movement solution for agentic application.
Any specific way you would like to sync data from BigQuery? Airbyte has you covered.
UI: Create connections and custom connectors in minutes.
API: Programmatic interactions, data syncing, and embedded connectors.
Terraform: Integration with CI/CD tools and rapid deployment with Infrastructure as Code.
PyAirbyte: Build LLM applications with Python libraries, SQL tools, and AI frameworks.
Flexible deployment options: self-hosted, cloud, and hybrid. Secure and compliant: ISO 27001, SOC 2, GDPR, HIPAA, data encryption, audit/monitoring, SSO, RBAC, and more. Centralized multi-tenant management with self-serve capabilities.
Trusted by the world's leading companies
Immediate ROI and productivity gains for your data teams.
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"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."
Raman Singh, Tech Lead at Symend
"The real ROI is in our ability to iterate quickly, especially at our increasing scale. At the end of the day, you want a tool like that to just work. We can forget about it and know that it's configured and it's connecting and it's working. That hands-free capability is a big appeal for the platform.”
Sean Carver, Director of Data at Petvisor
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Mondor La Grange, Head of BI and Data Engineering
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Amy Zhao, Senior Manager of Data Engineering at Peloton
"Airbyte allows us to stay flexible while scaling from hundred-million to billion-dollar enterprise clients."
Franziska Ibscher, Product Manager at Drivepoint
FAQs
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
What is BigQuery?
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
What data can you extract from BigQuery?
BigQuery provides access to a wide range of data types, including:
1
Structured data: This includes data that is organized into tables with defined columns and data types, such as CSV, JSON, and Avro files.
2
Semi-structured data: This includes data that has some structure, but not necessarily a fixed schema, such as XML and JSON files.
3
Unstructured data:This includes data that has no predefined structure, such as text, images, and videos.
4
Time-series data:This includes data that is organized by time, such as stock prices, weather data, and sensor readings.
5
Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and spatial databases.
6
Machine learning data:This includes data that is used to train machine learning models, such as labeled datasets and feature vectors.
7
Streaming data: This includes data that is generated in real-time, such as social media feeds, IoT sensor data, and log files.
Overall, BigQuery's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.
How do I transfer data from BigQuery?
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1
Set up BigQuery as a source connector (using Auth, or usually an API key)
2
Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3
Define which data you want to transfer from BigQuery and how frequently
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
What are top ETL tools to transfer data from BigQuery?
The most prominent ETL tools to transfer data to BigQuery include:
Airbyte
Fivetran
StitchData
Matillion
Talend Data Integration
These tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into BigQuery and other databases, data warehouses and data lakes, enhancing data management capabilities.
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
BigQuery Integration Guides
Connect your favorite tools and services to BigQuery
Sync your data from any source to BigQuery
Get your BigQuery data in whatever tools you need
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Github
Unstructured
Gitlab
Unstructured
Google Drive
Unstructured
Microsoft OneDrive
Unstructured
Microsoft Sharepoint
Unstructured
Notion
Unstructured
S3
Unstructured
Slack
Unstructured
Apify Dataset
Unstructured
Azure Blog Storage
Unstructured