Design the right data model for what the application actually needs, move it between cloud architectures with a cutover measured in seconds and a backup plan that has been restore-tested — then leave monitoring in place so slow queries, replication lag, and capacity limits are caught before users feel them.
What's included
Engage any one of these on its own, or run them as a single thread from schema design through to a monitored production database.
We start from access patterns, not tables: read/write ratio, query shapes, consistency needs, growth rate, and retention. That drives the conceptual, logical, and physical model — normalisation where integrity matters, denormalisation where latency does, and sharding/partitioning keys chosen up front.
Requirements → schemaRelational (PostgreSQL, MySQL), document (MongoDB), key-value and wide-column (DynamoDB, Cassandra), in-memory (Redis), analytical (ClickHouse, BigQuery, Redshift), or time-series — matched to the workload, with a clear rationale and the trade-offs written down rather than assumed.
Polyglot persistenceSelf-managed ↔ managed (RDS, Aurora, Cloud SQL, Azure Database), one cloud to another, or one engine to another. Logical replication and change-data-capture keep source and target in lockstep so the cutover window is seconds, not hours — with a rehearsed rollback if a check fails.
CDC · near-zero downtimeAutomated snapshots plus point-in-time recovery, cross-region copies, and defined RPO/RTO targets — then the part most teams skip: a scheduled, documented restore drill that proves the backups actually come back.
PITR · tested restoresDashboards and alerting for the signals that matter: slow and lock-contended queries, replication lag, connection-pool saturation, cache hit ratio, disk and IOPS headroom, and error rates — wired to your existing Prometheus/Grafana, CloudWatch, or Datadog with sane thresholds, not noise.
ObservabilityEncryption at rest and in transit, least-privilege roles and row-level security where it applies, audit logging, secret rotation, private networking, and a review against the benchmark relevant to your data — folded into the design, not bolted on later.
Hardened by defaultHow the work is structured
Entities and relationships, the queries the application will really run, expected volume and growth, latency and consistency targets, compliance constraints, and reporting needs — captured before any schema is drawn.
Conceptual → logical → physical model, indexing and partitioning strategy, and the engine choice with its rationale. Where more than one store fits, the split of responsibilities between them is made explicit.
Instance sizing, storage class and IOPS, read-replica and connection-pooling topology, and an honest month-one cost estimate — with the levers you can pull later if the shape of the load changes.
Schema conversion, an initial bulk load, then continuous replication / CDC to tail the delta. Application changes for dual-read or dual-write are identified early so the cutover itself is a small, well-understood step.
Row counts, checksums, and targeted query comparisons confirm parity. Cutover runs to a timed runbook with a go/no-go gate and a rollback that points the application back at the source in seconds.
DR configured and restore-tested, dashboards and alerts live, runbooks for the common failure modes written, and a walkthrough with the team that will operate it.
Why it matters
A schema that does not match how the application queries it shows up months later as slow pages, lock storms, and migrations that need downtime. Choosing the engine after the fact means rewrites. And a backup that has never been restored is a hope, not a recovery plan — the failure everyone assumes is covered until the day it isn't.
Getting the model, the migration, and the recovery story right at the start — and leaving monitoring behind so drift is visible — is far cheaper than unpicking any of them under pressure once real data and real users are on it.
What to expect
Turn requirements and access patterns into a schema and an engine choice, with the trade-offs documented.
Schema conversion, replication approach, application changes, and a validation and rollback plan.
Bulk load, continuous CDC sync, then row-count, checksum, and query parity checks against source.
Seconds-long cutover with live rollback, DR restore-tested, dashboards and alerts handed over.
Tell us about the application and the data — we will come back with a model, an engine recommendation, and a migration plan.