[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-10-09 UTC."],[[["This page provides resources and tools related to exporting Google Analytics 4 data to BigQuery, as discussed in the Google I/O 2023 talk."],["Explore links to sample datasets, schemas, basic queries, setup instructions, cost controls, and reporting tools to maximize your use of BigQuery with Google Analytics 4 data."],["Learn how to optimize BigQuery performance and costs by using features like scheduled queries, materialized views, partitioning, clustering, and BI Engine."],["Understand potential discrepancies between Google Analytics UI and BigQuery export numbers and find clarifying information."],["Engage with the Google Analytics community via the Discord server and developer newsletter for updates and support."]]],["The core content provides resources for utilizing BigQuery with Google Analytics. Key actions include exploring a sample dataset, using the BigQuery sandbox, and referencing the event schema. It also includes running sample queries, setting up BigQuery export, implementing cost controls, and using Looker Studio for reporting. Guidance is offered on managing BigQuery performance through scheduled queries, materialized views, partitioning, clustering, and BI Engine, with additional resources provided through discord and a newsletter.\n"]]
