What's changed: Created Associate Cloud Engineer Chapter 2 (Domain 2 "Planning and configuring": choosing compute = Compute Engine/GKE/Cloud Run/Cloud Functions/Spot VM/custom machine types; choosing storage and databases = Cloud SQL/Spanner/Firestore/Bigtable/BigQuery/Cloud Storage classes/Persistent Disk; planning networking = regions-zones/load balancers/Network Service Tiers).
2.2Choosing storage and databases
Understand how to choose among Google Cloud data services—relational (Cloud SQL, Spanner), NoSQL (Firestore, Bigtable), analytics (BigQuery), and object storage (Cloud Storage)—and how to pick Cloud Storage classes (Standard, Nearline, Coldline, Archive) and Persistent Disk types.
The right data service depends on type and use case. Choose by "transactions or analytics," "relational or NoSQL," and "what scale."
2.2.1Choosing a database
| Service | Kind | Typical use |
|---|---|---|
| Cloud SQL | Managed relational | MySQL/PostgreSQL/SQL Server apps |
| Spanner | Global distributed relational | Global, strongly consistent core systems |
| Firestore | Document NoSQL | Mobile/web apps |
| Bigtable | Wide-column NoSQL | IoT, time series, huge scale |
| BigQuery | Serverless data warehouse | SQL analytics at scale |
For transactions, use Cloud SQL (MySQL/PostgreSQL/SQL Server-compatible) for typical apps and Spanner for global strong consistency and high availability. For NoSQL, Firestore for mobile/web and Bigtable for huge-scale, low-latency IoT/time series. For analytics, use the serverless data warehouse BigQuery. Map "business DB = Cloud SQL, global core = Spanner, app NoSQL = Firestore, huge scale = Bigtable, analytics = BigQuery."
2.2.2Object storage and disks
Unstructured data (images, video, backups) goes in Cloud Storage (object storage). Choose a storage class by access frequency: Standard for frequent, Nearline for ~monthly, Coldline for ~quarterly, and Archive for long-term, rarely accessed data (rarer access = lower storage price but higher retrieval cost). Block storage attached to VMs is Persistent Disk, chosen by use case (zonal/regional for redundancy, standard/SSD).
Common: use case → service/class. E.g., "MySQL-compatible business DB" = Cloud SQL; "global strongly consistent core" = Spanner; "mobile app data" = Firestore; "huge IoT scale" = Bigtable; "SQL analytics at scale" = BigQuery; "store images/backups" = Cloud Storage; "~monthly access" = Nearline; "long-term rarely accessed" = Archive; "block disk for a VM" = Persistent Disk.
Watch the mix-ups: (1) BigQuery (analytics = DWH) vs Cloud SQL/Spanner (transactions) differ in role. (2) Cloud Storage is object storage, not block disk (Persistent Disk). (3) For storage classes, less access = cheaper storage but pricier retrieval—putting frequently accessed data in Coldline/Archive is more expensive.
2.2.3Section summary
- Business = Cloud SQL / global core = Spanner / app NoSQL = Firestore / huge scale = Bigtable / analytics = BigQuery
- Cloud Storage is object; classes = Standard/Nearline/Coldline/Archive (less access = cheaper storage, pricier retrieval)
- Block storage for VMs is Persistent Disk (zonal/regional, standard/SSD)
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Quick check
(just a quick review)Q1. Which suits a typical business app needing a managed relational DB compatible with MySQL/PostgreSQL?
Q2. Which relational database provides global strong consistency and high availability for core systems?
Q3. Which data warehouse analyzes petabyte-scale data with SQL?
Q4. Which Cloud Storage class best suits data rarely accessed but kept long term?
Q5. Which service best stores unstructured data such as images, video, and backups?
Q6. Which NoSQL handles huge-scale, low-latency time-series data from millions of IoT devices?