What's changed: Created Professional Data Engineer Chapter 4 (Domain 4 "Prepare and use for analysis": visualization prep = Looker/Data 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, Data 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."
Common: requirement → means. E.g., "speed up dashboards" = BI Engine/materialized views; "precompute common aggregations" = materialized views; "mask sensitive columns" = data masking; "detect sensitive data" = Cloud DLP; "control who sees which columns/rows" = column-/row-level access control.
Watch the mix-ups: (1) Materialized views (precompute) and BI Engine (in-memory acceleration) are distinct (combinable). (2) Do not forget protecting sensitive data in analytics (masking/column-row access). (3) For slow queries, suspect scan volume first (partitioning/clustering).
4.1.3Section summary
- Connect BI (Looker/Data Studio); accelerate with BI Engine/materialized views
- Address slow queries via scan/joins/partitioning-clustering
- Protect analytical data with IAM, Cloud DLP, data masking, and column/row access control
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Quick check
(just a quick review)Q1. To precompute common aggregations and speed up BigQuery dashboards, which is best?
Q2. Which feature provides in-memory acceleration for BigQuery analytics?
Q3. To mask sensitive columns in analytical data, which is best?
Q4. To detect PII and protect analytical data, what do you use?
Q5. A BigQuery query is slow. What should you suspect first?
Q6. To finely control who sees which columns/rows, what do you use in BigQuery?
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