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. set up BigQuery as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

BigQuery as a Source

BigQuery is a cloud-based data warehousing and analytics platform that allows users to store, manage, and analyze large amounts of data in real-time. It is a fully managed service that eliminates the need for users to manage their own infrastructure, and it offers a range of features such as SQL querying, machine learning, and data visualization. BigQuery is designed to handle petabyte-scale datasets and can be used for a variety of use cases, including business intelligence, data exploration, and predictive analytics. It is a powerful tool for organizations looking to gain insights from their data and make data-driven decisions.

BigQuery as a Destination

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. You can also explore Google BigQuery Sandbox—an all-in-one solution for storing, managing, and analyzing large datasets with ease and efficiency.
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Prerequisites

1. A BigQuery account to transfer your customer data automatically from.
2. A BigQuery account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including BigQuery and BigQuery, for seamless data migration.

When using Airbyte to move data from BigQuery to BigQuery, it extracts data from BigQuery using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their BigQuery data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up BigQuery as a source connector

1. First, you need to have a Google Cloud Platform account and a project with BigQuery enabled.

2. Go to the Google Cloud Console and create a new service account with the necessary permissions to access your BigQuery data.

3. Download the JSON key file for the service account and keep it safe.

4. Open Airbyte and go to the Sources page.

5. Click on the "Create a new source" button and select "BigQuery" from the list of available sources.

6. Enter a name for your source and click on "Next".

7. In the "Connection Configuration" section, enter the following information:  
- Project ID: the ID of your Google Cloud Platform project  
- JSON Key: copy and paste the contents of the JSON key file you downloaded earlier  
- Dataset: the name of the dataset you want to connect to

8. Click on "Test Connection" to make sure everything is working correctly.

9. If the test is successful, click on "Create Source" to save your configuration.

10. You can now use your BigQuery source connector to extract data from your dataset and load it into Airbyte for further processing.

Step 2: Set up BigQuery as a destination connector

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.

Step 3: Set up a connection to sync your BigQuery data to BigQuery

Once you've successfully connected BigQuery as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select BigQuery from the dropdown list of your configured sources.
3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific BigQuery objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from BigQuery to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your BigQuery data.

Use Cases to transfer your BigQuery data to BigQuery

Integrating data from BigQuery to BigQuery provides several benefits. Here are a few use cases:

1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your BigQuery data, extracting insights that wouldn't be possible within BigQuery alone.
2. Data Consolidation: If you're using multiple other sources along with BigQuery, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: BigQuery has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: BigQuery provides robust data security features. Syncing BigQuery data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding BigQuery data.
6. Data Science and Machine Learning: By having BigQuery data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While BigQuery provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a BigQuery table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a BigQuery account as an Airbyte data source connector.
2. Configure BigQuery as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from BigQuery to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!