Page Summary
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This page provides tools and resources referenced in the IO 2023 talk on BigQuery export for Google Analytics.
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Resources include a sample dataset, BigQuery sandbox, schema reference, sample queries, and pricing information.
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You can find instructions for setting up BigQuery export, learning about cost controls, and using reporting tools like Looker Studio.
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The page also covers managing performance and cost in BigQuery using features like scheduled queries, materialized views, partitioning, clustering, and BI Engine.
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Links to traffic source and UserID export data will be added when available, and you can join the GA Discord Server or sign up for the developer newsletter.
This page contains links to all tools and resources referenced in the IO 2023 talk on BigQuery export for Google Analytics.
Tools and resources
- Explore the BigQuery sample dataset for Google Analytics ecommerce web implementation to learn about the schema and the data structure.
- Try out BigQuery at no cost with the BigQuery sandbox.
- Use the schema reference to understand what's included in your BigQuery event export data.
- Try out sample basic queries on the demo dataset or your own BigQuery export dataset.
- View BigQuery pricing details.
- Review instructions for setting up BigQuery export for your Google Analytics property.
- Learn about BigQuery cost controls and how to implement them.
- Use Looker Studio and other third party reporting tools to create dashboards from your Google Analytics BigQuery export data.
- Understand why Google Analytics UI and BigQuery Export number might not match.
- Manage performance and cost in BigQuery:
- Links to collected traffic source data export and UserID-level export will be added here once these features are available.
- Join the GA Discord Server and sign up for the developer newsletter.