What's changed: Created Professional Data Engineer Chapter 4 (Domain 4 "Prepare and use for analysis": visualization prep = Looker/Looker Studio, materialized views/BI Engine, slow-query handling, data masking/IAM/Cloud DLP/column-row access; AI-ML prep and sharing = BigQuery ML/feature engineering, embeddings/RAG, Analytics Hub).
4.1Preparing data for visualization
Understand connecting BI tools, speeding up with precomputation (materialized views, BI Engine), troubleshooting slow queries, and protecting analytical data with security, data masking, IAM, and Cloud DLP.
Stored data creates value only when analyzed and visualized. Data engineers prepare data so BI is fast and secure.
4.1.1Visualization and acceleration
Connect BI tools (Looker, Looker Studio) to BigQuery for visualization. To accelerate, use materialized views (precomputed/aggregated) and BI Engine (in-memory acceleration for BigQuery). Precomputing common metrics speeds dashboards. Troubleshoot slow queries via scan volume, joins, and partitioning/clustering. Map "speed up dashboards = BI Engine/materialized views."
4.1.2Protecting analytical data
Analytical data also needs protection. Use IAM least privilege for access, discover sensitive data with Cloud DLP (Sensitive Data Protection), and control exposure with data masking (column-level/dynamic masking). In BigQuery, column-/row-level access control finely governs "who sees which columns/rows." Map "protect sensitive data even in analytics = masking/IAM/Cloud DLP."
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