A team migrates three years of transaction logs into a single Cloud Storage bucket, leaves everything in the Standard class because "we might need to look at any of it," and the storage bill for data nobody has opened in over a year quietly becomes one of the largest line items on the account. This pillar covers how to actually store and query data in Google Cloud effectively - choosing the right storage class, the right Persistent Disk type, and the right database product for a given workload's actual shape.
What This Pillar Covers
- Choosing between Cloud Storage classes (Standard, Nearline, Coldline, Archive) based on real access patterns
- Choosing between zonal and regional Persistent Disks for Compute Engine workloads
- Choosing between Cloud SQL, Firestore, Spanner, Bigtable, and BigQuery for a given data shape
- Running analytical queries against large datasets with BigQuery
- Automating storage cost savings with Lifecycle Management rules
- Migrating existing databases into GCP with Database Migration Service
Who This Is For
Cloud administrators, backend developers, and data engineers responsible for choosing appropriate storage and database services, and keeping storage costs proportional to actual access patterns.
Why This Matters in Production
Choosing BigQuery as a live transactional database, or Firestore for a workload that genuinely needs complex relational joins, are both common and costly mismatches discovered only after application code already assumes a query pattern that database was never built for. Matching the data's actual shape and access pattern to the right product from the start avoids an expensive mid-project migration later.
Prerequisites
- Completion of GCP Fundamentals and Resource Governance, or equivalent familiarity with Projects and IAM
- Basic understanding of relational versus NoSQL data models
- Familiarity with the gcloud CLI for hands-on practice