Google Cloud Explained: Compute, Databases, Storage, Networking, and IAM
Google Cloud is easiest to understand by grouping services around a few core infrastructure jobs rather than memorizing its entire product catalog.
Compute
Common choices include:
- Cloud Run — fully managed containerized services and jobs.
- GKE — managed Kubernetes.
- Compute Engine — VMs with OS/infrastructure control.
Google's current compute guidance recommends choosing the highest abstraction that still gives the control your workload needs.
Databases and Storage
Use the data service that matches the workload:
- Cloud SQL — managed MySQL, PostgreSQL, and SQL Server.
- Spanner — horizontally scalable relational workloads needing its distributed model.
- Firestore / Bigtable — non-relational workloads with different access patterns.
- Cloud Storage — durable object storage for files, backups, media, and large blobs.
- BigQuery — analytical warehouse rather than an OLTP application database.
Messaging
Pub/Sub provides asynchronous event delivery and decouples producers from consumers. It is useful for background processing, event-driven services, and data pipelines.
Networking
Core building blocks include VPC networking, Cloud Load Balancing, Cloud DNS, Cloud CDN, Cloud NAT, and Cloud Armor.
Keep private services private where practical and expose only intentional public entry points.
Identity
Use IAM and workload/service identities instead of long-lived credentials embedded in applications. Apply least privilege to both humans and workloads.
Well-Architected Thinking
Google Cloud's current framework evaluates workloads across operational excellence, security, reliability, cost, performance, and sustainability.
The best architecture is rarely the one using the most Google Cloud products; it is the one that meets those goals with the least unnecessary operational complexity.
Final Takeaway
Start with a small mental map: compute, data, storage, networking, messaging, identity, and observability. Choose services by workload requirements, then use the Well-Architected Framework to review the trade-offs around reliability, security, performance, and cost.
References
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