The managed database lineup
Six products covering genuinely different data shapes and scale profiles, each earning its complexity only for the specific problem it solves.
Speaker notes
- Cloud SQL (MySQL/Postgres/SQL Server) scales to 128 vCPUs and 864 GB RAM, comfortable for the large majority of relational workloads. AlloyDB is Postgres-compatible but roughly 4x faster for transactional and up to 100x faster for analytical queries, at a real 1.5-2x cost premium that needs a measured bottleneck to justify.
- Spanner earns its complexity specifically for workloads needing BOTH global distribution AND strong consistency simultaneously — a financial ledger that must never show inconsistent balances across regions is the canonical case, not general scale ambition.
- Firestore fits semi-structured, document-shaped application data with real-time sync; Bigtable fits massive-scale wide-column time-series and point lookups (the product Meridian's GPS pipeline would graduate to at much greater fleet scale); BigQuery is the serverless analytics warehouse already anchoring route-efficiency analytics and billing export.
Deck map
01
GCP foundations in one line
02
How the ACE exam maps to this course
03
GCP's geography and the project as the unit of isolation
04
Organization, folders, and projects
05
Org policies and how inheritance works
06
Labels, network tags, and resource manager tags
07
Billing accounts, budgets, and cost control
08
gcloud CLI, Cloud Shell, and client libraries
09
Terraform on GCP and the end of Deployment Manager
10
Gemini CLI, Cloud Assist, and Application Design Center
11
Principals, roles, bindings, and policies
12
Primitive, predefined, custom roles, and IAM conditions
13
Service accounts, keys, and impersonation
14
Workload Identity Federation
15
Break-glass access and IAM auditing
16
Choosing a machine family: E2, N4, C4
17
Disks, OS Login, and VM Manager
18
Spot VMs and managed instance groups
19
Autoscaling policies, health checks, GPUs and TPUs
20
GKE Autopilot vs Standard vs the 2026 hybrid option
21
Node pools and pod autoscaling
22
Cloud Run revisions, traffic splitting, and functions
23
Choosing between GKE, Cloud Run, and Compute Engine
24
Cloud Storage classes and lifecycle management
25
Choosing a managed database
26
The managed database lineup
27
Connection pooling, read replicas, and Pub/Sub
28
Backup, regional failover, and CMEK
29
VPC networks, subnets, and Shared VPC
30
Firewall rules, tags, and Cloud NAT
31
Load balancers, Cloud DNS, and Cloud CDN
32
VPN, Interconnect, and private access
33
How a request actually reaches shipment-api
34
Cloud Monitoring: metrics, dashboards, alerts
35
Cloud Logging: router, buckets, and audit logs
36
Trace, Profiler, Error Reporting, and Managed Prometheus
37
How the five Cloud Operations products fit together
38
Readiness checklist