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We start from evidence: slow-query logs, execution plans for the heaviest endpoints, table and index sizes, cache hit ratios, connection counts and where transfer volume is actually generated. This usually reveals a small number of queries responsible for most of the load, which is far more useful than a general audit of everything.
We check whether the model matches how the data is really used: where normalisation is protecting integrity, where a deliberate denormalisation would remove an expensive join, and where missing constraints or vague data types are quietly allowing bad rows in. Multi-tenant systems get extra scrutiny, because isolation must be enforced by the schema and not by remembering to add a filter.
Indexes are added for queries that exist, not for columns that look important, and unused indexes are removed because each one costs write throughput and storage. Alongside that we fix the classic offenders: N+1 access patterns, reading every column of a wide table, per-row round-trips that belong in a single aggregate statement, and pagination that scans the whole table to reach page fifty.
Serverless and edge deployments open connections in a way traditional pooling was never designed for, and the failure mode is a sudden wall of connection errors under load rather than a gradual slowdown. We size the pool, place a pooler correctly, keep transactions short, and move read-heavy work to cached or replicated paths where it makes sense.
Schema and platform migrations are planned as reversible steps — add the new structure, write to both, backfill, switch reads, then remove the old — so there is always a way back. We rehearse on a copy with production-sized data, measure how long each step actually takes, and agree the cutover window and rollback trigger before anyone touches production.
We verify backup schedule, retention and point-in-time recovery by performing a real restore, then document the recovery procedure, the monitoring signals worth alerting on, and the schema itself. The goal is that your team can operate the database without us, and choose to keep us for the harder work rather than out of dependency.
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