CLOUD DATABASE ENGINEERING

The database is only one layer of the production system.

Modern AWS database engineering spans architecture, migration, automation, monitoring, performance, security boundaries, and recovery evidence.

AUR

Amazon Aurora PostgreSQL

Architecture, endpoints, failover, upgrades, parameter strategy, performance engineering, backup/recovery, and operational resilience.

MY

Amazon Aurora MySQL

MySQL-compatible cluster architecture, endpoints, failover, reader scaling, binlog/CDC, capacity, upgrades, and evidence-led operations.

DMS

AWS DMS

Assessment, source/target endpoints, full-load and CDC patterns, validation, LOB considerations, throughput analysis, and migration observability.

IaC

Infrastructure Automation

Repeatable provisioning and operational workflows using infrastructure-as-code and orchestration rather than undocumented console-only changes.

OBS

Observability

Cloud metrics, database telemetry, logs, workload evidence, performance analysis, recovery signals, and actionable diagnostics.

DOC

Amazon DocumentDB

MongoDB compatibility assessment, document and index migration, DMS change data capture, cutover validation, and recovery engineering.

01Assesssource, target, workload
02Buildrepeatable infrastructure
03Migratefull load + CDC
04Validatedata + performance
05Operateobserve + recover

AWS DATABASE DECISION LAB

Amazon RDS or Aurora?
Flip the requirement—not a coin.

Amazon RDS supports multiple relational database engines, including Amazon Aurora. The practical decision is whether Aurora’s cloud-native architecture fits the workload better than another RDS engine.

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Source discipline · AWS documentation, checked September 2026