Connectors

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BigQuery

Data replication

Connect BigQuery with any data sources.

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.

  • Certified
  • Generally available
Load data into BigQueryRead the docs
BigQuery

One connector

Everything BigQuery can do in Airbyte

  • Load from any source

    Move data into BigQuery from 600+ Airbyte sources on a schedule you control.

  • Incremental syncs

    Pull only the records that changed since the last run instead of reloading everything.

  • Cloud or self-hosted

    Run the BigQuery connector on Cloud, Self-Managed Enterprise.

Specifications

What to know before you load into BigQuery

  • Support levelCertified
  • Available onCloud, Self-Managed Enterprise
  • Connector version3.0.22
  • Release stageGenerally available

Sync capabilities

  • Full Refresh SyncSupported
  • Incremental SyncSupported
  • Sources600+ Airbyte connectors

Set up in 14 steps

  1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
  2. Scroll down until you find the "BigQuery" destination connector and click on it.
  3. Click the "Create Destination" button to begin setting up your BigQuery destination.
  4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
  5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
  6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
  7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
  8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
  9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
  10. Follow the prompts to enter your source credentials and configure your sync settings.
  11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
  12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
  13. Suggested Read:
  14. Bigquery Source

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Common questions

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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.

BigQuery provides access to a wide range of data types, including: Structured data (organized into tables with defined columns and data types, such as CSV, JSON, and Avro files); Semi-structured data (some structure, but not necessarily a fixed schema, such as XML and JSON files); Unstructured data (no predefined structure, such as text, images, and videos); Time-series data (organized by time, such as stock prices, weather data, and sensor readings); Geospatial data (related to geographic locations, such as maps, GPS coordinates, and spatial databases); Machine learning data (used to train machine learning models, such as labeled datasets and feature vectors); and Streaming data (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.

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Suggested Read:

Bigquery Source

The most prominent ETL tools to transfer data to 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 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.

Start moving BigQuery data today

Free for 14 days on Airbyte Cloud. Set up the BigQuery connector once and let Airbyte keep it in sync.

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