PostgreSQL databases designed, optimized, and operated by senior engineers who've built data systems for Setu, Pine Labs, and KredX.
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PostgreSQL schema design that survives your next ten features, not just the current sprint. Normalized tables where consistency matters, denormalized views where read performance matters. Proper use of PostgreSQL-specific types: JSONB for flexible data, arrays for tags, range types for scheduling, and composite types for structured fields. We design schemas that make your queries simple and your migrations painless.
Slow PostgreSQL queries diagnosed and fixed. We use EXPLAIN ANALYZE, pg_stat_statements, and auto_explain to find exactly where time is spent. Index strategy designed around your actual query patterns, not textbook rules. Partitioning for tables with millions of rows. Connection pooling with pgBouncer configured for your concurrency profile. We have taken query response times from 12 seconds to under 50 milliseconds on production systems.
Migrating from Oracle, SQL Server, MySQL, or MongoDB to PostgreSQL. Schema translation, stored procedure conversion, data migration with validation, and application-layer query rewrites. We run source and target databases in parallel during cutover so rollback is always an option. Zero-downtime migrations using logical replication for systems that cannot afford maintenance windows.
PostgreSQL deployed and configured on AWS RDS, Aurora PostgreSQL, or Google Cloud SQL. Instance sizing based on workload analysis, not guesswork. Automated backups, point-in-time recovery, read replicas for scaling reads, and parameter group tuning for your specific access patterns. We configure the managed service so you get the reliability without the operational overhead.
Streaming replication, logical replication, and failover configuration for systems that need 99.99% uptime. Hot standby replicas for read scaling, synchronous replication for zero data loss, and automated failover with Patroni or cloud-native solutions. We design the replication topology around your RPO and RTO requirements, not a one-size-fits-all template.
PostgreSQL as the source or destination in your data pipeline. Change data capture with Debezium, real-time sync to Elasticsearch or data warehouses, and ETL/ELT pipelines feeding analytics. pgvector for AI embedding storage and similarity search, enabling vector queries alongside your relational data without a separate vector database.
The database schema is the most durable part of your system. Application code gets rewritten, but table structures persist for years. We invest time in getting the data model right because fixing a schema mistake in production costs 10x more than getting it right upfront.
We do not add indexes based on intuition. EXPLAIN ANALYZE, pg_stat_statements, and workload analysis tell us exactly where time is spent. Every optimization is measurable, and we show you the before and after numbers.
Running PostgreSQL on bare metal is rarely worth the operational cost. AWS RDS or Aurora handles backups, patching, failover, and monitoring. We configure the managed service correctly so your team focuses on application logic, not database operations.
Foreign keys, check constraints, unique indexes, and NOT NULL where appropriate. The database should reject bad data, not leave validation to the application layer alone. Constraints are documentation that the database enforces automatically.
No proprietary extensions, no vendor lock-in patterns, no undocumented schema decisions. Your team or any qualified DBA can take over operations. Complete documentation covering every schema choice and its rationale.
We analyze your data requirements, access patterns, and growth projections. You get a technical proposal covering schema design, indexing strategy, hosting recommendation (self-managed vs. RDS vs. Aurora), replication topology, and backup policy. No implementation until the data model is right.
Entity-relationship diagrams, table definitions with constraints, index strategy documented, and migration scripts written. For database migrations, we map source-to-target schema differences and build the data validation suite. Your development team can start building against the schema immediately.
Database provisioned, schemas deployed, application integration built. Performance testing with production-like data volumes from the start. Query optimization happens during development, not after launch. We benchmark every critical query path against your SLA targets.
Simulated production load with realistic data volumes and concurrency. Connection pool sizing verified. Vacuum and autovacuum tuned. Monitoring configured with pg_stat_statements, CloudWatch or Datadog. Nothing goes live until the database handles your peak load with headroom.
Complete documentation covering schema decisions, index rationale, replication setup, and operational runbooks. Your team or DBA owns the database entirely. Optional support retainer for ongoing optimization, but no lock-in.
Engineers with 5+ years of production PostgreSQL experience spanning schema design, query optimization, replication, and cloud deployment. Not application developers who write SQL on the side.
A team covering database architecture, application integration, and DevOps. Full ownership of data layer design, implementation, migration, and production operations.
Fixed-scope engagement for specific database projects: migrations, performance audits, or schema redesigns. Clear deliverables, timeline, and transparent pricing.
Talk directly with engineers, not sales. We will assess fit and give honest next steps.
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PostgreSQL development costs vary by scope. A performance audit and optimization for an existing database typically runs $8,000 to $25,000. A greenfield database design with application integration costs $30,000 to $80,000. A full database migration from Oracle or SQL Server to PostgreSQL ranges from $50,000 to $200,000+ depending on schema complexity, stored procedure conversion, and data volume. Procedure offers a free architecture consultation to scope your project.