Cloud-Native Databases: Choosing the Right Database for Your Workload
Choosing the right database is crucial. This guide compares AWS database services.
Use Cases:
- Traditional applications
- SQL queries
- ACID compliance needed
- Complex relationships
Engines:
- MySQL
- PostgreSQL
- MariaDB
- SQL Server
- Oracle
Use Cases:
- High-scale applications
- NoSQL requirements
- Serverless applications
- Simple data models
Features:
- Fully managed
- Auto-scaling
- Global tables
- On-demand pricing
Use Cases:
- High-performance needs
- MySQL/PostgreSQL compatibility
- Multi-region replication
- Auto-scaling
Features:
- 5x faster than MySQL
- 3x faster than PostgreSQL
- Automatic failover
- Continuous backup
| Database | Type | Scalability | Use Case |
|---|
| RDS | SQL | Vertical | Traditional apps |
| DynamoDB | NoSQL | Horizontal | High-scale apps |
| Aurora | SQL | Both | High-performance |
- Choose based on data model
- Consider scalability needs
- Factor in cost
- Evaluate performance requirements
- Plan for backup and DR
Choose RDS for traditional SQL, DynamoDB for scale, and Aurora for high-performance SQL workloads.
For Cloud-Native Databases: Choosing the Right Database for Your Workload, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Cloud-Native Databases: Choosing the Right Database for Your Workload, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Cloud-Native Databases: Choosing the Right Database for Your Workload, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
