Warehouse, Lake and Platform Design
This topic covers data warehouse modeling, normalization, BigQuery architecture, data lake management, Dataplex, Dataplex Catalog, Cloud Storage, and federated governance.
How to study for Google Professional Data Engineer
Treat each item as a data workload decision: identify source, sink, velocity, schema, governance, storage pattern, processing mode, and operational risk.
Core concepts
Concept 1
Warehouse, Lake and Platform Design questions test data engineering design decisions across ingestion, storage, analysis, automation, governance, and reliability.
Exam cue: Identify the source, sink, processing mode, data model, access pattern, freshness requirement, and governance boundary.
Concept 2
The best answer maps data shape, velocity, quality, access pattern, compliance, processing model, and operations needs to the right Google Cloud service.
Exam cue: Choose the service pattern that satisfies batch, streaming, analytics, ML, storage, security, and operational requirements.
Concept 3
Eliminate answers that ignore schema evolution, late data, IAM, regional constraints, lineage, cost, quotas, or recovery behavior.
Exam cue: Prefer managed, observable, repeatable, secure, cost-aware, and fault-tolerant data pipelines when requirements support them.
Risk pitfalls and guardrails
Choosing a storage system without checking query pattern, latency, consistency, cost, and lifecycle requirements.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Treating streaming data like batch data when event time, windows, and late arrivals matter.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Ignoring data governance, privacy, monitoring, or automation until after the pipeline is built.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Memory anchors
Data Warehouse
A data warehouse organizes curated structured data for analytical querying and reporting.
Data Lake
A data lake stores raw or semi-processed data at scale for flexible analysis and processing.
Data Model
A data model defines entities, relationships, grain, schema, and query support.
Normalization
Normalization reduces redundancy but can affect query simplicity and performance.
Materialized View
A materialized view stores precomputed query results to improve performance for repeated queries.
Dataplex
Dataplex helps manage, govern, and organize distributed data across lakes and warehouses.
Dataplex Catalog
Dataplex Catalog stores metadata for discovery, governance, and data understanding.
Federated Governance
Federated governance applies consistent controls across distributed data domains.
Lifecycle Policy
A lifecycle policy manages retention, movement, or deletion of stored data.
Cost Control
Cost control aligns storage, processing, and query choices to business value.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Why is partitioning a large BigQuery table by date usually beneficial?
How do partitioning and clustering complement each other in BigQuery?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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