Assembling a data platform out of Google Cloud's storage and catalog tools
Building a data platform on Google Cloud means matching storage and processing services to workload requirements: BigQuery for structured analytics at scale, Cloud Storage for raw/unstructured data and data lakes, and Dataplex to unify governance, discovery, and quality across distributed data. The exam expects you to select and configure the right combination of tools based on data structure, access patterns, latency, cost, and governance needs.
1 · Learn the must-know
- Cloud Storage is the default landing zone for raw, semi-structured, or unstructured data and supports storage classes (Standard, Nearline, Coldline, Archive) to optimize cost based on access frequency.
- BigQuery is the primary choice for structured/semi-structured analytical data requiring SQL access, native partitioning/clustering, and separation of storage and compute for scalable querying.
- Dataplex provides a unified governance layer over data lakes (Cloud Storage) and warehouses (BigQuery) by organizing assets into lakes, zones, and assets without moving or copying the underlying data.
- Dataplex Catalog automatically harvests technical metadata from BigQuery, Cloud Storage, and other sources, and lets you enrich it with business metadata (tags, aspects) for discovery and lineage.
- A common exam pattern is a lakehouse architecture: land raw data in Cloud Storage, curate/query it via BigQuery (including BigLake tables for querying Cloud Storage data with BigQuery semantics), and govern both through Dataplex.
- Choosing the wrong storage tier or skipping governance setup (e.g., no Dataplex zones/catalog) is a common exam trap, requirements around data quality, access control, and discoverability should drive tool selection, not just storage cost.
2 · Check your understanding
A retailer's Cloud Storage buckets and BigQuery datasets are spread across a dozen projects, one per business unit. Analysts cannot easily find which dataset holds a given subject area, and there is no single place to apply consistent data quality checks or access policies across both storage systems. The team wants to organize these existing assets logically without physically moving or duplicating any data. Which approach best meets this requirement?
What you have tried across GCP PDE's objectives, not a readiness score.
Designing data processing systems~22% of the exam0 of 17 tried
Ingesting and processing the data~25% of the exam0 of 11 tried
Storing the data~20% of the exam0 of 13 tried
Preparing and using data for analysis~15% of the exam0 of 11 tried
Maintaining and automating data workloads~18% of the exam0 of 15 tried
3 · Keep going
Ready for more? Take a weighted mock or try free practice questions.