Comparison of AWS vs GCP database services, mapped by category so you can quickly relate them—especially for migration or architecture decisions.
🔹 Relational Databases (OLTP)
Use Case AWS GCP
Managed MySQL/Postgres/SQL Server Amazon RDS Cloud SQL
High-performance Postgres Amazon Aurora AlloyDB for PostgreSQL
👉 Key difference:
Aurora is more mature and widely adopted
AlloyDB is newer but optimized heavily for PostgreSQL performance
🔹 Globally Distributed SQL (Horizontal Scaling)
Use Case AWS GCP
Global relational DB ❌ (No direct equivalent) Cloud Spanner
👉 GCP clearly leads here
Spanner = strong consistency + global scale
AWS has no true equivalent (Aurora Global ≠ same architecture)
🔹 NoSQL Databases
Type AWS GCP
Key-Value / Document DynamoDB Firestore
Wide-column (Big data) ❌ Bigtable
👉 Differences:
DynamoDB = extremely scalable, serverless
Firestore = easier for app dev (real-time sync)
Bigtable = niche but powerful (IoT, analytics scale)
🔹 Data Warehouse / Analytics
Use Case AWS GCP
Analytics / OLAP Amazon Redshift BigQuery
👉 Key difference:
BigQuery = fully serverless, no infra management
Redshift = cluster-based (though serverless option exists now)
🔹 In-Memory / Caching
Use Case AWS GCP
Redis / Memcached Amazon ElastiCache Memorystore
👉 Almost equivalent services
🔹 Migration Services
Use Case AWS GCP
DB Migration AWS DMS Database Migration Service
DMS → Full load + simple migrations
Continuous replication AWS DMS+CDC Datastream (Real-time CDC)
🔹 Quick Decision View
Lift & Shift (Oracle/Postgres/MySQL)
→ AWS: RDS
→ GCP: Cloud SQLHigh-performance Postgres
→ AWS: Aurora
→ GCP: AlloyDBMassive global scale (banking, fintech)
→ GCP wins with: Cloud SpannerServerless analytics
→ GCP (BigQuery) is simplerNoSQL at scale
→ AWS DynamoDB (more mature ecosystem)
