Getting a dashboard or report in front of the people who need it

For the Professional Data Engineer exam, you need to know how to publish data insights using Google Cloud's native BI tools, primarily Looker Studio and Looker, and how these tools connect to and query underlying data sources efficiently. Understanding the trade-offs between direct connections, extracts, and cached data is key to designing performant, cost-effective reporting solutions.

1 · Learn the must-know

• 1Looker Studio (formerly Data Studio) connects directly to BigQuery, Cloud SQL, Sheets, and other sources, and can cache query results to reduce redundant queries and costs.
• 2BigQuery BI Engine is an in-memory analysis service that accelerates SQL queries from Looker Studio and other BI tools by caching data in memory, reducing latency for dashboards.
• 3Looker uses LookML, a modeling layer, to define reusable metrics and dimensions, enabling governed, consistent reporting across an organization rather than duplicating SQL logic in each report.
• 4For large or frequently accessed datasets, materializing results into a BigQuery table (rather than querying raw data live) can significantly reduce dashboard load times and query costs.
• 5Row-level and column-level security should be enforced at the data warehouse level (e.g., BigQuery authorized views, row-level access policies) rather than solely in the BI tool, to ensure consistent governance across all reporting layers.
• 6Scheduling data refreshes (via scheduled queries, Cloud Composer, or Dataform) ensures reports reflect current data without requiring live queries on every dashboard view, balancing freshness against cost and performance.

2 · Check your understanding