How to load data from Metabase to BigQuery

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Metabase 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 Metabase 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 Metabase 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.
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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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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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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.

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Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

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“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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"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: Export Data from Metabase

Begin by exporting the data you need from Metabase. Use Metabase's query editor to run your desired query. Once you get the results, use the 'Download' option to export the data as a CSV file. Ensure you have the necessary permissions to export data and check the file for any anomalies or errors after downloading.

Open the exported CSV file and ensure that the data format is compatible with BigQuery. Clean up any discrepancies, such as incorrect data types or missing values. Save the file ensuring it meets UTF-8 encoding standards, which is typically a requirement for data uploads into BigQuery.

Access the Google Cloud Console and create or select a project where you want your BigQuery dataset to reside. Ensure that billing is enabled for the project, as BigQuery operations may incur costs. Take note of the project ID, as you’ll need it for subsequent steps.

In the BigQuery section of the Google Cloud Console, create a new dataset to store your data. Within this dataset, create a new table specifying the schema to match the structure of your CSV file. You can define the schema manually or use the schema auto-detection feature if you are unsure of the exact types.

Before importing the CSV file into BigQuery, upload it to Google Cloud Storage (GCS). Navigate to the GCS section of the Google Cloud Console, create a bucket if one doesn’t exist, and upload the CSV file into this bucket. Make sure the uploaded file is accessible for the import process by setting the appropriate permissions.

Go back to the BigQuery section and use the 'Create Table' feature. Select 'Google Cloud Storage' as the source, and choose the appropriate bucket and file. Configure the import settings, making sure the field delimiter matches your CSV file (usually a comma). Use the schema defined in Step 4, and start the import process.

Once the data import is complete, run a few validation queries to ensure that the data in BigQuery matches the original data in Metabase. Check for discrepancies in record counts and verify that all fields are correctly populated. If needed, perform transformations or adjustments using SQL within BigQuery to align the dataset with your analytical needs.
By following these steps, you can effectively move your data from Metabase to BigQuery without relying on third-party connectors or integrations.