Domain 2 of 6 · Chapter 2 of 3

Data Management Solutions

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Included in this chapter:

  • How to read the Google Cloud data portfolio
  • The six core data products and when each wins
  • Cloud Storage classes: same storage, different cost profile
  • Exam-pattern recognition

Google Cloud data product selection by data shape and use case

ProductData modelRelational / NoSQLPrimary use case
Cloud StorageObjects (files/blobs)Neither (object storage)Unstructured files, media, backups, data-lake storage
Cloud SQLRelational tablesRelational (SQL)Regional transactional apps on MySQL / PostgreSQL / SQL Server
SpannerRelational tablesRelational (SQL)Global, horizontally scalable, strongly consistent transactional workloads
FirestoreDocumentsNoSQLServerless app data with real-time sync for mobile / web / server
BigtableWide-column key-valueNoSQLMassive low-latency, high-throughput time-series / IoT / operational data
BigQueryColumnar tablesRelational (SQL) warehouseServerless analytics and ML over large historical / multi-cloud datasets

Decision tree

Unstructured objects(files, media, backups)?YesCloud StorageNoAnalytics over largehistorical data?YesBigQuery+ Omni for other cloudsNoRelational data,queried with SQL?Yes (SQL)Global horizontal scale+ high availability?YesSpannerNoCloud SQLNo (NoSQL)Document data withreal-time sync?YesFirestoreNoBigtabletime-series / high-throughputPick by data shape first, then transactional vs. analytical.
Cheat sheet

Cheat sheet

  • Pick the data product by data shape first
  • Separate transactional workloads from analytical ones
  • Use Cloud Storage for unstructured object data
  • Use Cloud SQL for standard regional relational apps
  • Use Spanner when relational data needs global scale
  • Use Firestore for app data with real-time sync
  • Use Bigtable for massive high-throughput key-value data
  • Use BigQuery for analytics over large historical data
  • BigQuery ML brings machine learning into SQL
  • BigQuery Omni analyzes data in other clouds
  • Cloud Storage classes differ only in cost profile
  • Match the storage class to access frequency
  • BigQuery is a warehouse, not an application backend
  • Relational scale is what splits Cloud SQL from Spanner
  • Managed services shift effort from running to using data
  • BigQuery public datasets are free to store, pay only to query

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References

  1. Cloud Storage documentation
  2. Cloud SQL overview
  3. Spanner
  4. Firestore documentation
  5. Cloud Bigtable overview
  6. BigQuery introduction
  7. Cloud Storage classes