> For the complete documentation index, see [llms.txt](https://documentation.grax.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.grax.com/reuse-data/data-lake/gcp-data-lakehouse.md). # GCP Data Lakehouse The recommended data architecture on GCP is: * Data Format: Parquet * Storage: Google Cloud Storage (GCS) * ETL: DataFlow * Lakehouse: BigQuery The recommended workflow is: * GRAX writes data to GCS in Parquet * Data Flow * Notified when new Parquet is available * Python script reads new Parquet data * Extracts objects and fields for downstream * Transforms fields into computed fields for business logic * Loads into Big Query * Big Query * Queries and joins multiple data sets * GRAX ETL data * GRAX datalake data * Additional data sets Anti-patterns are: * Reading entire Parquet files vs specific columns * Polling for new Parquet vs getting a push notification * Moving GRAX data to track ingestion --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://documentation.grax.com/reuse-data/data-lake/gcp-data-lakehouse.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.