TL;DR

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 MySQL 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.

What is BigQuery

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.

What is MySQL

MySQL is an SQL (Structured Query Language)-based open-source database management system. An application with many uses, it offers a variety of products, from free MySQL downloads of the most recent iteration to support packages with full service support at the enterprise level. The MySQL platform, while most often used as a web database, also supports e-commerce and data warehousing applications, and more.

‍

{{COMPONENT_CTA}}

‍

Prerequisites

1. A BigQuery account to transfer your customer data automatically from.
2. A MySQL 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 MySQL, for seamless data migration.

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

Methods to Move Data From Bigquery to mysql

• Method 1: Connecting Bigquery to mysql using Airbyte.
• Method 2: Connecting Bigquery to mysql manually.

Method 1: Connecting Bigquery to mysql using Airbyte

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 MySQL as a destination connector

1. First, you need to have a MySQL database set up and running. Ensure that you have the necessary credentials to access the database.
2. Log in to your Airbyte account and navigate to the "Destinations" tab.
3. Click on the "Add Destination" button and select "MySQL" from the list of available connectors.
4. Enter the necessary details such as the host, port, username, password, and database name. Ensure that the details are accurate and match the credentials you have for your MySQL database.
5. Test the connection to ensure that Airbyte can successfully connect to your MySQL database. If the connection is successful, you will receive a confirmation message.
6. Once the connection is established, you can configure the settings for your MySQL destination connector. You can choose to enable or disable certain features such as SSL encryption, bulk loading, and more.
7. You can also set up the schema mapping for your MySQL database. This involves mapping the fields from your source data to the corresponding fields in your MySQL database.
8. Once you have configured the settings and schema mapping, you can start syncing data from your source to your MySQL database. You can choose to run the sync manually or set up a schedule for automatic syncing.
9. Monitor the sync process to ensure that data is being transferred accurately and efficiently. You can view the sync logs and troubleshoot any issues that may arise.
10. Congratulations! You have successfully connected your MySQL destination connector on Airbyte and can now start syncing data from your source to your MySQL database.

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

Once you've successfully connected BigQuery as a data source and MySQL 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 MySQL 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 MySQL. 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 MySQL according to your settings.

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

Method 2: Connecting Bigquery to mysql manually

Moving data from Google BigQuery to MySQL without using third-party connectors or integrations involves several steps, including exporting data from BigQuery, preparing the MySQL database, and importing data into MySQL. Below is a step-by-step guide for developers:

Step 1: Export Data from BigQuery

1. Access BigQuery: Log in to your Google Cloud Platform (GCP) account and access the BigQuery console.

2. Prepare the Data for Export: Ensure your data is in a format that can be exported and imported into MySQL. For example, BigQuery supports exporting data in CSV, JSON, or Avro format.

3. Export the Data:

    - Navigate to your dataset and select the table you want to export.

    - Click on the "Export" button and choose the desired export format (e.g., CSV).

    - Specify the GCS (Google Cloud Storage) bucket where you want to store the exported data.

    - Set the export preferences, such as the file name and whether to allow field delimiters within data.

    - Start the export job and wait for it to complete.

4. Download the Exported Data:

    - Once the export job is complete, navigate to the GCS bucket where the data was exported.

    - Download the exported files to your local machine.

Step 2: Prepare the MySQL Database

1. Install MySQL: If you haven't already, install MySQL on the desired server or use a managed MySQL service.

2. Create a Database and User:

    - Log in to the MySQL server using a client or the command line.

    - Create a new database for the imported data: `CREATE DATABASE bigquery_data;`

    - Create a user with the necessary privileges: `CREATE USER 'bigquery_user'@'%' IDENTIFIED BY 'password';`

    - Grant the user privileges on the new database: `GRANT ALL PRIVILEGES ON bigquery_data.* TO 'bigquery_user'@'%';`

    - Flush the privileges to ensure they are applied: `FLUSH PRIVILEGES;`

3. Create Tables:

    - Define the schema for the tables in MySQL based on the schema from BigQuery.

    - Create tables in MySQL using the `CREATE TABLE` statement.

    - Make sure the data types in MySQL match the data types in the BigQuery dataset.

Step 3: Import Data into MySQL

1. Prepare for Import:

    - If you exported data in CSV format, ensure the CSV file is ready for import (e.g., correct delimiter, no header row if not needed, etc.).

2. Import the Data:

    - Use the MySQL command-line tool or a client to connect to the MySQL server.

    - Select the database: `USE bigquery_data;`

    - Use the `LOAD DATA INFILE` command to import the CSV file into the MySQL table:

      ```

      LOAD DATA LOCAL INFILE '/path/to/your/exported-file.csv'

      INTO TABLE your_table_name

      FIELDS TERMINATED BY ','

      OPTIONALLY ENCLOSED BY '"'

      LINES TERMINATED BY '\n'

      IGNORE 1 LINES;  // Use this if your CSV has a header row

      ```

    - Adjust the command parameters as needed to match your data format.

3. Verify the Import:

    - Run some queries to ensure the data was imported correctly.

    - Check for any errors or inconsistencies and address them as needed.

Step 4: Clean Up

- Remove Temporary Files: After verifying the import, delete any temporary files from your local machine and GCS bucket to prevent storage costs and maintain security.

- Review Security Settings: Ensure the MySQL user created for the import has appropriate permissions and that the database is secure.

Additional Notes:

- The above steps assume a simple data export and import. Complex data types or nested structures in BigQuery may require additional processing before import.

- Always back up your MySQL database before performing large imports.

- The `LOAD DATA INFILE` command may require additional permissions or settings changes in MySQL, especially the `local-infile` setting.

- Make sure that the character encoding (e.g., UTF-8) is consistent between BigQuery exports and MySQL imports to avoid data corruption.

By following these steps, developers should be able to move data from BigQuery to MySQL without using third-party connectors or integrations. Always test the process with a small subset of data before proceeding with the full dataset to ensure everything works as expected.

Use Cases to transfer your BigQuery data to MySQL

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

1. Advanced Analytics: MySQL’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 MySQL 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 MySQL allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: MySQL provides robust data security features. Syncing BigQuery data to MySQL ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: MySQL 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 MySQL, 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 MySQL, providing more advanced business intelligence options. If you have a BigQuery table that needs to be converted to a MySQL 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 MySQL as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from BigQuery to MySQL 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!