Chapter 3 of 5 · Google Cloud Professional Data Engineer
Storing the data
Choosing storage, BigQuery warehouse design, data lakes with BigLake and Dataplex.
- 4topics
- 20 minfast track
- 2diagrams and exhibits
- 20%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Selecting Storage Systems by Access PatternPick BigQuery, BigLake, Bigtable, Spanner, Cloud SQL, AlloyDB, Cloud Storage, Firestore or Memorystore by access pattern; plan…Diagram5 min
- 2Designing a BigQuery Data WarehouseBigQuery warehouse design: star schemas, nested and repeated fields, how far to normalize, mapping business needs, partitioning…5 min
- 3Managing a Data Lake with Cloud Storage and BigLakeRun a data lake on Cloud Storage: zones, discovery, BigLake access control, cost controls, processing with Dataproc, Dataflow and…Diagram5 min
- 4Data Platform Design and Federated GovernanceBuild a data platform with Dataplex, Dataplex Catalog, BigQuery and Cloud Storage, and design federated, data mesh governance for…5 min
About this chapter
What this chapter covers
Storing the data is about 20% of the Google Cloud Professional Data Engineer exam. It covers where data should live, how a BigQuery warehouse is modelled, how a data lake on Cloud Storage is managed, and how these pieces become one governed data platform.
It covers selecting storage systems (access patterns, BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore and Memorystore, cost, performance and lifecycle), planning a data warehouse (data model, normalization, business requirements and access patterns), using a data lake (discovery, access, cost controls, processing and monitoring) and designing a data platform (Dataplex, Dataplex Catalog, BigQuery, Cloud Storage and federated governance).
The exam has 40 to 50 multiple choice and multiple select questions in 2 hours. Storage questions describe how data is written and read, its size and its latency target, then ask "What should you do?"; one access-pattern detail usually decides the product.
How the topics fit together
Choose the store from the access pattern, then design inside it. The lake and the warehouse are not rivals: BigLake and Dataplex put one set of security and metadata over both.
Learn one decisive phrase per product: "SQL analytics at scale" is BigQuery, "millisecond key lookups at huge volume" is Bigtable, "global strong consistency" is Spanner, "existing MySQL or PostgreSQL" is Cloud SQL.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with tables and code examples.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Google Cloud Professional Data Engineer question bank has 733 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.