Amazon RDS and Aurora designed for the failure you have not had yet — sized honestly, backed up verifiably, and recoverable in a timeframe you have actually measured.
Most of an application can fail partially. The database usually cannot. It is also the component most often sized once at launch, upgraded reluctantly, and backed up according to a policy nobody has tested end to end.
We design RDS and Aurora deployments around recovery objectives you have stated out loud, with the failover and restore paths exercised before you need them rather than discovered during an incident.
RDS or Aurora, provisioned or Serverless v2, sized against real query and connection patterns instead of the instance class you started on.
Multi-AZ, read replicas, and backup strategy mapped to stated RPO and RTO targets — then tested against them.
Moves from self-managed or on-premises databases, plus major-version upgrades planned around your maintenance windows.
A backup you have never restored is a hypothesis. We run restore drills, measure how long a recovery genuinely takes against your data volume, and write down the result — so the number in your continuity plan reflects something observed rather than something assumed. For regulated environments, those drills produce evidence alongside the outcome.
Database instances get over-provisioned because nobody wants to be the person who under-sized production. We use Performance Insights and real workload data to find where the headroom actually sits, apply RDS Proxy where connection churn is the real constraint, and plan reserved capacity once the steady state is clear rather than committing to a guess.
Workload discovery, migration planning and sequencing, database and data movement, and cutover with validation.
Multi-account AWS environments for federal, state, and local work, designed against FedRAMP, NIST, and CMMC control sets with evidence collection built in from the start.
Infrastructure as code, continuous compliance monitoring, and automated qualification evidence for regulated Life Sciences workloads on AWS.
We'll review your database configuration, backup strategy, and recovery timings, and tell you plainly where the gap is between your plan and your capability.
Request a Review