PostgreSQL Development
Access precise database implementations, optimization and tuning with PostgreSQL, Deliver optimal performance with PostgreSQL DBA's for ease of operations and reliability without sacrificing security
PostgreSQL Delivery Snapshot
Current release — ACID, CTE, window functions
Automatic HA failover — sub-30s RTO
Real query plans — actual rows & costs
Managed cloud — auto-scaling, auto-backup
"Ahex redesigned our PostgreSQL schema and rewrote our 15 worst-performing queries. Average API response time dropped from 2.4 seconds to 180ms. We moved to Patroni HA with automatic failover and our first real production failover completed in 18 seconds with zero data loss. The most thorough PostgreSQL engagement we've been through."
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End-to-End PostgreSQL Development Services
Ahex Technologies is your go-to partner for enterprise PostgreSQL development. With deep expertise in PostgreSQL schema design, InnoDB engine architecture, B-tree, GIN, GiST, BRIN, and partial index strategy, query optimisation with EXPLAIN ANALYZE and pg_stat_statements, PL/pgSQL stored procedures and triggers, JSONB for document-hybrid workloads, PostGIS for geospatial applications, table partitioning with pg_partman, logical replication, streaming replication, Patroni with etcd or Consul for automatic HA and failover, pgBouncer for connection pooling, pg_upgrade for major version upgrades, Amazon Aurora PostgreSQL, Amazon RDS PostgreSQL Multi-AZ, Google Cloud SQL for PostgreSQL, Azure Database for PostgreSQL Flexible Server, TimescaleDB for time-series, and Citus for horizontal sharding — we deliver PostgreSQL databases that perform reliably under production load and scale with your application.
Our PostgreSQL services span the full database lifecycle — from greenfield schema design and Django/Rails/Node.js stack integration to long-term DBA retainers, performance tuning engagements, HA cluster setup, cloud migration, and dedicated PostgreSQL engineer teams embedded in your organisation. Whether you need a Patroni HA cluster for a mission-critical application, a slow database tuned to sub-50ms query times, a PostgreSQL to Aurora migration, or a pgBouncer layer to eliminate connection exhaustion at scale — our certified database engineers deliver solutions that meet your availability and performance requirements.
Engineering
JSONB · PostGIS
pg_partman
pg_stat_statements
Cloud SQL · Supabase
Sub-30s RTO
Transaction Mode
ACID-Compliant, Extensible, and SQL-Standards-Leading: The Foundation Behind Every High-Performance PostgreSQL Application
EXPLAIN ANALYZE Gives You Actual Query Execution Data — Not Estimates
PostgreSQL's EXPLAIN ANALYZE executes the query and returns the actual rows, actual loops, actual time, and buffer hit/miss statistics for every node in the query plan — not the planner's estimates. Combined with pg_stat_statements for aggregate query statistics across all executions, Ahex engineers identify the exact queries causing production slowdowns, the exact indexes missing, and the exact join strategies the planner is choosing — with measured data, not guesswork.
JSONB + GIN Indexing Gives You Document Database Performance Inside a Relational Schema
PostgreSQL's JSONB column type stores JSON in a decomposed binary format — GIN indexes on JSONB columns support containment queries (@>), key existence (?), and jsonpath expressions with the same index performance as B-tree indexes on scalar columns. JSONB eliminates the need for a separate document store (MongoDB, DynamoDB) for hybrid relational/document workloads, while preserving full ACID guarantees, JOIN capability, and foreign key constraints across the entire schema.
Patroni + etcd Delivers Automatic PostgreSQL HA Without Manual DBA Intervention
Patroni manages streaming replication topology, automatic leader election via etcd or Consul, automatic failover in under 30 seconds, and health monitoring with configurable TTL — used by Zalando, GitLab, and Booking.com in production. Applications connect through HAProxy or pgBouncer — when the primary fails, Patroni promotes a replica without DBA interface drift between client and server becomes a compile error, not a production bug at 2am.
PostGIS Makes PostgreSQL the Most Capable Geospatial Database Available
PostGIS extends PostgreSQL with geometry and geography types, spatial indexes (GiST on geometry columns), and over 800 spatial functions — ST_Within, ST_Distance, ST_Intersects, ST_Buffer, ST_Transform, and full GeoJSON import/export. PostGIS is the reference implementation for spatial SQL and powers geographic applications at every scale — from property search radius queries to national-scale logistics routing to satellite telemetry analysis.
Table Partitioning + pg_partman Handles Billions of Rows Without Manual Maintenance
PostgreSQL declarative partitioning with pg_partman automating partition creation, retention, and detachment. Query planner pruning eliminates partitions not matching the WHERE clause, reducing large table scans to single-partition seeks. pg_partman's background worker creates future partitions on schedule and detaches expired partitions without locking the parent table.
The database powering Apple, Instagram, Spotify, Twitch, and the UK Government Digital Service — and the open-source database of choice for engineering teams that need superior SQL standards compliance, advanced indexing, JSONB, PostGIS, and the most extensible extension ecosystem in the relational database world.
Our PostgreSQL Development Services
From greenfield PostgreSQL schema design and query optimisation to Patroni HA, pgBouncer, PostGIS, Aurora migration, TimescaleDB, and long-term DBA retainers.
PostgreSQL Schema Design & Architecture
Greenfield PostgreSQL schema design — normalised schema with proper primary keys, foreign keys, and check constraints, appropriate data types, JSONB for flexible attributes, custom domains and enum types, table partitioning strategy, extension selection, and an index strategy designed alongside the schema from day one.
Normalised schema — proper PKs, FKs, check constraints, custom types and domains
JSONB columns for flexible attributes — GIN indexed for containment queries
Table partitioning — RANGE, LIST, HASH with pg_partman for automated management
Extension strategy — PostGIS, pg_partman, pg_trgm, unaccent, uuid-ossp selected per project
Row-Level Security — policies enforcing multi-tenant data isolation at the database layer
PostgreSQL Query Optimisation
Systematic PostgreSQL query optimisation — pg_stat_statements for aggregate slow query identification, EXPLAIN (ANALYZE, BUFFERS) for actual execution plan review, index strategy design, SARGable predicate rewrites, partial and expression indexes, statistics target tuning, and work_mem adjustment for sort-heavy queries.
pg_stat_statements — top queries by total_time, mean_time, calls ranked
EXPLAIN (ANALYZE, BUFFERS) — actual rows, actual time, buffer hit/miss per node
Partial indexes — WHERE clause indexes reducing index size 80–95% on filtered queries
Statistics target — ALTER TABLE ... ALTER COLUMN ... SET STATISTICS for better planner estimates
Patroni HA & Streaming Replication
PostgreSQL HA architecture — Patroni with etcd or Consul for automatic leader election and failover, streaming replication to multiple standbys, synchronous replication for zero-data-loss failover on write-critical workloads, HAProxy or pgBouncer as the application endpoint, and pg_rewind for fast replica resynchronisation after failover.
Patroni — automatic leader election, failover, and replica management via etcd
Synchronous replication — synchronous_standby_names for zero-data-loss commits
Logical replication — table-level replication for upgrades and cross-cluster data feeds
pg_rewind — fast replica resync after network partition without full base backup
pgBouncer & Connection Pooling
pgBouncer deployment for PostgreSQL connection management — transaction-mode pooling for Django/Rails/Node.js applications, multiplexing thousands of application connections to a small server-side pool, and transparent failover routing through Patroni. Eliminates the "FATAL: sorry, too many clients already" error at scale.
Transaction-mode pooling — connection released after each transaction, not session end
Pool sizing — server-side pool tuned to max_connections budget on the PostgreSQL instance
Patroni integration — pgBouncer reconfigured automatically after Patroni failover
PgBouncer Exporter — Prometheus metrics for pool saturation and client wait time
Zod Validation Layer
PostGIS geospatial PostgreSQL development — geometry and geography columns, GiST spatial indexes, spatial query optimisation, coordinate system management, GeoJSON import/export, and integration with QGIS, Mapbox, and Leaflet map libraries.
Geometry and geography columns — ST_Distance, ST_Within, ST_Intersects, ST_Buffer
GiST spatial indexes — spatial query performance comparable to B-tree on scalar columns
GeoJSON import/export — ST_AsGeoJSON, ST_GeomFromGeoJSON for API integration
React Hook Form + Zod resolver integration
ESLint Strict Configuration
PostgreSQL cloud deployment — Amazon Aurora PostgreSQL, Amazon RDS PostgreSQL Multi-AZ, Google Cloud SQL for PostgreSQL, Azure Database for PostgreSQL Flexible Server, and Supabase. AWS DMS or pglogical for online migration with minimal downtime cut-over.
Amazon Aurora PostgreSQL — up to 3× throughput, 15 read replicas, Global Database
RDS Multi-AZ — automatic failover, automated backups, minor version upgrades
AWS DMS + pglogical — online migration with near-zero downtime cut-over
Supabase — managed PostgreSQL with built-in auth, real-time, and Storage API
Code Audit & Consulting
Ongoing PostgreSQL DBA retainer — monthly pg_stat_statements review, VACUUM and autovacuum tuning, index bloat monitoring with pgstattuple, replication lag monitoring, connection pool utilisation, WAL disk management, backup verification, and a named PostgreSQL DBA on Slack for P1 incidents.
Monthly pg_stat_statements review — top queries by total_time ranked and reviewed
VACUUM / autovacuum tuning — table bloat, dead tuple accumulation, and freeze monitoring
Index bloat analysis — pgstattuple identifies bloated indexes for REINDEX CONCURRENTLY
Named PostgreSQL DBA on Slack — P1 incident response within defined SLA
PostgreSQL Consulting & Training
PostgreSQL major version upgrades — pg_upgrade for in-place upgrades (PG 12→14→16→17), logical replication upgrade path for near-zero downtime, compatibility testing, extension upgrade validation (PostGIS, TimescaleDB, pg_partman), and pg_dump/restore for smaller databases requiring cross-architecture migration.
pg_upgrade — in-place major version upgrade with link mode for large databases
Logical replication upgrade — PG 14→17 with near-zero downtime using publication/subscription
Extension compatibility — PostGIS, TimescaleDB, pg_partman version validation before upgrade
pg_upgrade compatibility report — 5-day turnaround before upgrade window is scheduled
Hire PostgreSQL DBA Engineers You Can Rely On
At Ahex Technologies, we don’t just write code — we own outcomes. From type architecture to post-launch monitoring, our PostgreSQL DBA team is your end-to-end database partner — responsive, transparent, and accountable.
3–5 days to onboard your dedicated PostgreSQL DBA engineer
Senior PostgreSQL DBA — Patroni HA, pgBouncer, pgBackRest, query optimisation, PostGIS, TimescaleDB, Aurora migration, and full production database managemental types
Direct Slack access to your actual engineer — no account managers
Named, consistent developer — no bait-and-switch
Full code ownership from day one — no lock-in
Timezone-aligned — UK, UAE, and US hours coverage
2-week replacement guarantee if it's not the right fit
Ahex PostgreSQL DBA Partnership Standard
3–5 days
100%
Day 1
2 weeks
3 zones
// Signed
Secure by Default. Hardened on Every PostgreSQL Engagement.
The following are the PostgreSQL security controls, database quality standards, and hardening benchmarks Ahex applies on every engagement — configured before the first table is created in production.
Principle of Least Privilege — No Application Role Has SUPERUSER or LOGIN to postgres
Every PostgreSQL engagement Ahex delivers applies least privilege — dedicated application roles with GRANT on specific schemas and tables only, never SUPERUSER; separate read-only roles for reporting connections routed via pgBouncer; the postgres superuser locked to local Unix socket connections only; and all privilege grants documented in the migration script history. (prev: no implicit any, no unsafe assignments, no unchecked indexed access.
Branded / Opaque Types
All application queries use parameterised query bindings — $1, $2... placeholders via psycopg2, asyncpg, node-postgres, or JDBC. PL/pgSQL functions use EXECUTE ... USING parameter binding for any dynamic SQL. SQL injection through the application data layer is structurally prevented on every Ahex PostgreSQL engagement.
Zod Input Validation
PostgreSQL data-at-rest encryption via pgcrypto for column-level encryption of sensitive fields, pg_tde (Transparent Data Encryption, PG 17) or filesystem-level encryption (LUKS on Linux, dm-crypt) for full-database encryption. In-transit encryption via TLS enforced with ssl=on and hostssl rules in pg_hba.conf — plain-text connections rejected. pg_audit logs all DDL, DML, and connection events to a centralised audit trail.
Discriminated Union Errors
Row-Level Security (RLS) policies enforce multi-tenant data isolation at the PostgreSQL engine level — each application role sees only the rows matching its tenant context, set via SET LOCAL app.current_tenant_id. RLS bypasses require SUPERUSER and are audited. Data isolation is enforced structurally — not by application-layer WHERE clauses that can be omitted.
Dependency Auditing
pgBackRest physical backup — full backups weekly, differential daily, WAL archival continuous. Point-in-time recovery (PITR) to any transaction within the WAL retention window. pgBackRest parallel backup and restore reduces large database RTO by 60–80% compared to pg_dump. Backup files encrypted with AES-256. Restore tested monthly to a secondary environment against a defined RTO target.
CI Type-Check Gate
pg_stat_statements enabled with pg_stat_statements.track=all — top queries by total_time, mean_time, stddev, and calls reported weekly. auto_explain with log_min_duration=200ms logs full EXPLAIN ANALYZE plans for slow queries automatically to the PostgreSQL log. VACUUM progress tracked via pg_stat_progress_vacuum to catch tables accumulating dead tuples.
Built to Compliance & Industry Standards
Our PostgreSQL engineers design databases that meet regulatory requirements across healthcare, finance, and data privacy — a typed codebase is also an auditable one.
HIPAA — PHI Encryption and Row-Level Security
PHI columns encrypted with pgcrypto column-level encryption — application-level AES-256 for the most sensitive health record fields. Row-Level Security policies restrict PHI rows to authorised clinical roles set via SET LOCAL app.current_role. pg_audit logs all SELECT and DML against PHI tables with user, timestamp, and query. TLS enforced via pg_hba.conf hostssl — no plain-text connections to databases containing PHI, separating PHI from non-sensitive data at the type level — misuse flagged at compile time, not discovered in an audit.
Healthcare platforms, patient portals, HL7/FHIR integrations PCI DSS Type Patterns
Opaque CardNumber and CVV types prevent raw payment strings being passed through un-validated code paths — enforced by the compiler, not just policy.
Payment platforms, financial dashboards, trading portals GDPR — PII at the PostgreSQL Layer
PostgreSQL views expose anonymised data to reporting roles — email addresses masked via regexp_replace, date-of-birth truncated to year, national ID replaced with a hash. The underlying PII columns are only accessible to the privileged application role — reporting roles query the view, never the base table. PII is structurally separated from anonymised data models — accidental exposure of personal data caught before runtime in production.
EU, UK, and India data-privacy regulated products SOC 2 — PostgreSQL Audit Logging
Typed event schemas ensure every audit log entry has a known, validated shape — no untyped JSON blobs in the compliance trail.
SaaS products targeting enterprise buyers with security reviews PCI DSS — Cardholder Data in PostgreSQL
Cardholder data encrypted with pgcrypto column-level encryption or application-level AES-256 before storage — plaintext PANs never stored. TLS enforced via pg_hba.conf hostssl rules. pg_audit captures all SELECT and DML against payment tables. Separate PostgreSQL role for payment schema with minimal GRANT. Network access restricted via pg_hba.conf host-based rules — only application servers permitted.
All PostgreSQL databases storing or processing payment card data ISO 27001 — PostgreSQL Hardening
PostgreSQL configuration hardened per CIS Benchmark — log_connections=on, log_disconnections=on, log_duration=on for long queries, password_encryption=scram-sha-256, ssl=on, local connections Unix-socket only for superuser, pg_hba.conf with minimal host rules, and all unused extensions uninstalled. OS-level PostgreSQL service account is a dedicated low-privilege postgres user.
All PostgreSQL instances in organisations pursuing ISO 27001 certification ISO 9001 — PostgreSQL Change Management
All PostgreSQL schema changes deployed via numbered migration scripts — Flyway, Liquibase, Alembic (Django), ActiveRecord migrations (Rails), or Prisma Migrate — reviewed via PR, applied to development and staging before production, and recorded in a deployment log. No direct production DDL changes outside the migration pipeline. EXPLAIN ANALYZE baseline captured for every migration that adds or removes an index.
Enterprise clients requiring certified development processes PostgreSQL Credential & Connection Security
PostgreSQL connection strings stored in AWS Secrets Manager, HashiCorp Vault, or environment-specific secrets management — never hardcoded in .env files committed to source control. RDS/Aurora PostgreSQL supports IAM database authentication — no long-lived passwords required. All passwords use SCRAM-SHA-256 authentication (password_encryption=scram-sha-256). password_authentication disabled in pg_hba.conf — md5 never used on Ahex-managed instances.
All Ahex-managed PostgreSQL instances with application connectivity PostgreSQL Performance SLAs & Monitoring
Query performance SLAs defined in terms of P95 query duration, replication lag threshold, and connection pool saturation — monitored via pganalyze, Datadog PostgreSQL integration, or Prometheus + Grafana with postgres_exporter. Alerts fire when mean query time exceeds threshold, replication lag exceeds 30 seconds, or active connections approach max_connections limit.
All PostgreSQL instances with defined query performance or availability SLAs The Full PostgreSQL Stack, Operated at Depth
From PostgreSQL 17 and Patroni to pgBouncer, pgBackRest, pg_partman, PostGIS, TimescaleDB, pganalyze, and Aurora PostgreSQL — every tool our PostgreSQL team uses daily on production databases.
Database Engine & Distributions
PostgreSQL versions and derivatives
Admin & Development Tools
PostgreSQL management tooling
HA, Replication & Pooling
High availability and connection management
Backup & Recovery
Backup strategy and PITR
Monitoring & Performance
Observability and tuning tooling
Extensions & Specialisations
PostgreSQL extension ecosystem
Schema Migration & ORM
Migration tooling and application ORMs
Deployment & Infra
Getting typed code to production
PostgreSQL vs MySQL vs MongoDB vs Oracle — An Honest Comparison
We work with all four. We give honest advice — including recommending MySQL for PHP/Laravel LAMP stacks and teams wanting the broadest cloud-managed service support, MongoDB for pure document workloads without relational requirements, and Oracle for large enterprises already committed to the Oracle licensing stack.
| Criteria | PostgreSQL | MySQL | MongoDB / Oracle |
|---|---|---|---|
| SQL standards compliance | Best-in-class open-source compliance — window functions, CTEs (properly optimised in PG 12+), lateral joins, recursive CTEs, FILTER clause on aggregates, DISTINCT ON, and full ANSI SQL support | Good — window functions and CTEs supported in MySQL 8.0; some ANSI deviations (ONLY_FULL_GROUP_BY quirks, limited LATERAL support) | MongoDB: no SQL, document query language only; Oracle: excellent SQL compliance, PL/SQL is most feature-rich procedural SQL |
| JSONB and document storage | JSONB — binary JSON with GIN indexing, @> containment operators, jsonpath queries, and generated columns for indexable JSON paths; full ACID and JOIN capability | JSON column type — JSON_EXTRACT, generated columns; less capable than JSONB, no GIN indexing equivalent | MongoDB: native document store — best-in-class for pure document workloads; no JOIN or FK; Oracle: JSON support added in 21c |
| Geospatial support | PostGIS — the reference implementation for spatial SQL; 800+ spatial functions, GiST spatial indexes, full coordinate system support; powers Mapbox, QGIS, and national-scale GIS applications | Spatial types built-in (geometry, point) but much more limited than PostGIS; no coordinate system transformations; limited spatial index support | MongoDB: geospatial queries with 2dsphere indexes — good for simple proximity queries; Oracle Spatial: comparable to PostGIS but proprietary and licensed |
| Extensibility | Most extensible relational database — custom types, operators, functions, index access methods, foreign data wrappers, and background workers. Supabase, TimescaleDB, Citus, and pgvector are all PostgreSQL extensions | Plugin architecture for storage engines (InnoDB, MyRocks) but much more limited extension ecosystem compared to PostgreSQL | MongoDB: limited extension model; Oracle: rich extension ecosystem but proprietary and expensive |
| High availability | Patroni + etcd — industry-standard HA with automatic failover, used by Zalando, GitLab, and Booking.com; logical replication for minimal-downtime upgrades | InnoDB Cluster (Group Replication + MySQL Router) — native HA without external tools; simpler GUI-driven setup than Patroni | MongoDB: built-in replica sets with automatic failover — easiest HA setup; Oracle: Data Guard — powerful but complex and expensive |
| Ahex recommendation | Best for: Python/Django, Ruby on Rails, Node.js, Go applications; JSONB document-hybrid workloads; geospatial applications requiring PostGIS; teams prioritising SQL standards compliance; complex analytical queries with CTEs and window functions; and cloud-native applications on Supabase or Neon | Best for: PHP/Laravel/WordPress/Magento LAMP stacks; teams wanting native InnoDB Cluster HA without etcd; and organisations needing the broadest cloud-managed service support across AWS, GCP, and Azure simultaneously | MongoDB: pure document workloads, content management, mobile backends without relational requirements; Oracle: large enterprises with existing Oracle licensing and complex PL/SQL codebases |
| Refactoring safety | Excellent — rename catches all usages | Risky — no static analysis | Good — better than JS, less than TS |
| Production bug reduction | ~40% fewer type-related bugs (strict) | Baseline | ~15% reduction (lenient) |
Type Contract First. Shared Architecture. Assessment First. Architecture Second. Zero Surprises in Production.
A PostgreSQL-specific engagement process — database assessment and query baseline before any change is made. Schema and index strategy documented before the first table is created. All changes applied to development and staging before production. pg_stat_statements baseline before and after every tuning engagement. Quality enforced at every phase, zero surprises in prompiler config defined before a single component is built. Safety enforced from sprint zero, not patched in retrospect.
PostgreSQL Health Assessment
Full PostgreSQL health check — pg_stat_statements top query analysis, EXPLAIN ANALYZE on the 10 slowest queries, index usage via pg_stat_user_indexes, table bloat via pgstattuple, autovacuum effectiveness, replication lag baseline, WAL generation rate, and postgresql.conf audit against pgconfig.org recommendations. Written assessment report delivered before engagement work begins.
Schema & Architecture Design
Database schema design — normalised PostgreSQL schema with proper PKs, FKs, and check constraints; JSONB for flexible attributes; PostGIS geometry for spatial data; table partitioning plan with pg_partman; HA topology selection (Patroni + etcd, streaming replication); pgBouncer architecture; extension selection; and Row-Level Security policy design for multi-tenant applications.
Development & Implementation
Schema deployed via Flyway/Alembic/Prisma Migrate numbered migration scripts. Stored procedures and functions in PL/pgSQL. Patroni + etcd cluster configuration. pgBouncer deployment and pool sizing. pgBackRest backup schedule. pg_partman background worker configuration. PostGIS geometry columns and GiST spatial indexes. All applied to development and tested before staging.
Tuning & Hardening
Query optimisation — EXPLAIN (ANALYZE, BUFFERS) review, index additions, partial index design, statistics target tuning, work_mem adjustment for sort-heavy queries. postgresql.conf tuning — shared_buffers, effective_cache_size, checkpoint_completion_target, wal_buffers. Security hardening — pg_hba.conf hostssl, SCRAM-SHA-256, least privilege roles, pg_audit configuration.
Staging Validation & Load Test
Full schema and data migration to staging. Application integration testing against staging PostgreSQL. pgbench or k6 load test at 2× expected peak concurrent connections. Query performance baseline comparison vs Phase 0 assessment. Patroni failover test — automatic promotion confirmed under simulated load. pgBackRest restore test — PITR recovery validated against defined RTO.
Monitoring & DBA Retainer
pganalyze or postgres_exporter + Grafana dashboards — slow query count, replication lag, connection pool saturation, autovacuum effectiveness, WAL generation rate. Monthly pg_stat_statements review. Quarterly pgBackRest restore test. Patroni failover drill. Named PostgreSQL DBA on Slack for P1 incidents. Monthly performance report delivered to client.
Three Ways to Work With Ahex PostgreSQL Engineers
All models include PostgreSQL health assessment report, numbered migration script pipeline, pgBackRest backup strategy, Patroni HA documentation, security hardening report, named PostgreSQL DBA engineers, and full ownership from day one.
FIXED BUDGET
Cost is locked in a fixed-scope model. Ideal when the roadmap is well-defined and you want budget certainty.
- Pay for specific milestones
- Pricing based on requirements
- Fixed delivery date
- Low management overhead
Billing
Fixed / Milestone
Specific, well-defined scope
Best For
A clear brief → build → ship
Dedicated Team
A dedicated pod you optimise, scale, and augment your in-house team with. Best for ongoing product development.
Best suited for teams that need predictable sprint velocity.
- Personalised team
- No lock-in
- Real-time updates
- Contributions & standups
Billing
Monthly retainer
Best For
Long-term, cost-optimised delivery
Model Fit
Team that owns the roadmap
Most Popular
TIME & MATERIAL
In this model, there is no fixed time or budget. You will pay for the actual hours worked or materials completed and used.
- Volume of your time
- Discuss and understand
- Pay per task
- Full flexibility on scope
Billing
Hourly rate card
Speed, scope, resource coordination
Best For
Evolving, flexible scope
Why Choose PostgreSQL Development
Your teams will ship faster, safer code — and your production systems will have fewer database incidents and significantly faster query times — when PostgreSQL is designed and maintained correctly from the first sprint.
Assessment-First — Every Recommendation Backed by pg_stat_statements Data
Ahex starts every PostgreSQL engagement with a full health assessment: pg_stat_statements top query analysis, EXPLAIN ANALYZE on the slowest queries, index usage review, table bloat analysis, autovacuum tuning review, and postgresql.conf audit. Every index recommendation is backed by actual pg_stat_statements execution data — not guesswork. Clients receive a written assessment report before any work begins.
Partial and Expression Indexes Solve Query Performance Problems MySQL Cannot
PostgreSQL's partial indexes (CREATE INDEX ... WHERE status = 'active') create indexes on a subset of rows — an index on active orders from a 100-million row orders table covers only 50,000 rows. PostgreSQL's expression indexes (CREATE INDEX ... ON (lower(email))) index the result of an expression, enabling case-insensitive email lookups to use an index seek. Neither feature exists in MySQL — they are PostgreSQL differentiators that solve query performance problems that require application-layer workarounds in other databases.
Flyway/Alembic/Prisma Migrate Schema Versioning Means Your PostgreSQL Schema Is in Source Control
All PostgreSQL schema changes deployed via numbered migration scripts committed to Git — reviewed in a PR, applied to development then staging, and executed in production via the CI/CD pipeline. No more "someone added a column directly in production." Schema drift between environments is eliminated. EXPLAIN ANALYZE baseline is captured before and after every migration that adds or removes an index, so performance regressions from migration changes are caught in staging.
Patroni Delivers Automatic Failover Without Manual DBA Intervention or Application Changes
Ahex configures Patroni on every PostgreSQL engagement requiring HA — streaming replication topology managed by Patroni, leader election via etcd, automatic failover in under 30 seconds. Applications connect through a pgBouncer or HAProxy endpoint — when the primary fails, Patroni promotes a standby and pgBouncer routes writes to the new primary. No DBA intervention required, no application configuration changes, no manual switchover scripts. Used by Zalando, GitLab, and Booking.com at production scale.
pganalyze Gives You Continuous PostgreSQL Performance Monitoring Without Manual Query Review
Ahex deploys pganalyze on every PostgreSQL DBA retainer — a purpose-built PostgreSQL monitoring platform that continuously collects pg_stat_statements data, identifies regression in query performance between deploys, surfaces index bloat and missing indexes, monitors VACUUM effectiveness, and tracks connection pool saturation via pgBouncer integration. pganalyze's automated EXPLAIN plan collection captures query plans without developer intervention — performance regressions are visible before customers notice them.
pgBackRest Delivers Hot Backups, Parallel Restore, and WAL Archival in a Single Tool
Ahex deploys pgBackRest on every PostgreSQL engagement as the primary backup tool — hot physical backups without pg_stop_backup complexity, WAL archival to S3 or GCS for continuous PITR, parallel restore that reduces RTO by 60–80% on large databases compared to pg_basebackup, AES-256 backup encryption, backup integrity verification with checksums, and delta restore for fast recovery from partial failures. Full backup weekly, differential daily, WAL archival continuous.
Row-Level Security Enforces Multi-Tenant Data Isolation at the Database Engine Level
Ahex implements PostgreSQL Row-Level Security policies on every multi-tenant SaaS engagement — each application role sees only the rows matching its tenant context, set via SET LOCAL app.current_tenant_id. RLS is enforced at the PostgreSQL engine level — it cannot be bypassed by an application bug, a missing WHERE clause, or a new API endpoint added without security review. Data isolation is structural, not procedural.
UK, UAE, USA, and Australia — PostgreSQL DBA Delivery Across Our Core Markets
Ahex has delivered PostgreSQL engagements for clients in the UK, UAE, USA, and Australia across SaaS, FinTech, healthcare, logistics, and geospatial applications. Our PostgreSQL engineers work in IST timezone with 4–6 hour overlap with UK and UAE business hours — health assessment reviews, architecture calls, and P1 production incident response all happen in your working hours.
We Type Faster with AI — So You Ship Sooner
Our PostgreSQL engineers use AI-powered tools across every phase — from type migration to test generation — without sacrificing type safety or code quality. The result: more output, fewer delays, the same rigorous strictness.
55%
Faster Type Migration
AI generates Zod schemas from JSON samples, infers types from existing JS, and suggests typed replacements for any casts — saving 2–3 days per migration sprint.
40%
Reduction in Review Cycles
AI-assisted code review flags unsafe type patterns, missing return types, and any-cast misuse before human review — fewer back-and-forth cycles and faster PR merges.
70%
Test Coverage Generated Automatically
PostgreSQL query optimisation, index recommendation analysis, and migration scripts auto-generated from Zod schemas and function signatures — QA phase starts with strong coverage.
30 %
Shorter Overall Delivery Time
Combined AI acceleration across all phases consistently cuts total delivery timelines by 25–35% without scope compromise.
GitHub Copilot + Claude Code
PostgreSQL query generation from business requirements, index recommendation review from EXPLAIN ANALYZE output, PL/pgSQL function scaffolding, and Flyway/Alembic migration script generation from schema comparison diffs. Every Ahex PostgreSQL engineer uses GitHub Copilot with PostgreSQL SQL dialect context — all AI-generated SQL is reviewed, tested against actual EXPLAIN ANALYZE plans in development, and verified in staging before production deployment.
PostgreSQL Query & Config Scaffolding
AI accelerates Flyway/Alembic migration script generation from schema diff output, pgBouncer pool sizing recommendation generation from pg_stat_activity analysis, and postgresql.conf tuning recommendation generation from pg_settings and system resource inventory — 50% of configuration boilerplate produced before implementation sprint, reviewed by a senior PostgreSQL engineer on every project.
AI-Generated PostgreSQL Documentation
PostgreSQL health assessment reports, index strategy rationale, Patroni architecture documentation, pgBouncer configuration runbooks, and DBA operational playbooks auto-generated from pg_stat_statements, pg_stat_user_indexes, and pg_stat_bgwriter output — always in sync with the actual production database state.
AI Query Plan Analysis
AI-assisted EXPLAIN ANALYZE interpretation — query plan output parsed to surface sequential scans, inefficient nested loop joins, missing index opportunities, and high-cost sort operations with SQL and index fix suggestions. PostgreSQL engineers verify every recommendation against pg_stat_statements before implementing in development. Shift-left performance on every PostgreSQL project.
All AI-generated PostgreSQL queries, Flyway/Alembic migration scripts, Patroni configuration, pgBouncer pool sizing, and postgresql.conf tuning recommendations are reviewed, tested in development, and owned by a named Ahex PostgreSQL Ahex engineer before it ships. We use AI to move faster — not to skip the health assessment phase, bypass the staging validation requirement, or deploy schema changes directly to production without a numbered migration script.
Six PostgreSQL Problems Every Development Team Encountersg Team Encounters
Every team running PostgreSQL in production hits these sooner or later. These are the problems our engineers diagnose repeatedly and know how to prevent from sprint zero.
Sequential Scan on a 200-Million Row Table — API Response Time 8 Seconds
Problem
A Django application's order history endpoint takes 8 seconds per request. pg_stat_statements identifies the query as the top consumer by total_time. EXPLAIN (ANALYZE, BUFFERS) on the query shows a Seq Scan on the orders table with rows=200,341,022 and actual time=7,841ms. The WHERE clause filters on user_id and created_at — but the only index on the table is the primary key (id). PostgreSQL is reading every row in the 200-million row table on every API call.
Solution
Ahex runs EXPLAIN (ANALYZE, BUFFERS) on the query — the sequential scan is confirmed with actual rows=200,341,022 and 98% of the cost at the Seq Scan node. A composite index on (user_id, created_at DESC) INCLUDE (status, total) is created with CREATE INDEX CONCURRENTLY — no table lock, no application downtime. EXPLAIN ANALYZE after index creation shows Index Scan with rows=47 and actual time=1.2ms. API response time drops from 8 seconds to 180ms. The index was created in 6 minutes with zero application downtime.
"FATAL: sorry, too many clients already" — Connection Exhaustion at Peak Load
Problem
A Node.js SaaS application connects directly to a PostgreSQL RDS instance. max_connections is set to 500. During peak usage, the application has 600+ concurrent Promises each holding a connection open — PostgreSQL starts rejecting new connections with "FATAL: sorry, too many clients already." Increasing max_connections to 1,000 causes RDS to run out of memory (each PostgreSQL connection consumes 5–10MB). The team has been upgrading to larger and more expensive RDS instance types but the problem returns at each new tier.
Solution
Ahex deploys pgBouncer in transaction-mode pooling between the application and PostgreSQL — 2,000 application connections are multiplexed to a server-side pool of 100 PostgreSQL connections. Each connection is released back to the pool after each transaction, not held for the duration of the application session. The application connection string is updated to point to pgBouncer on port 5432 — no application code changes required. After deployment: zero connection exhaustion errors across the following 90 days of peak usage. The RDS instance is downsized by two tiers, reducing the monthly AWS bill by £1,200/month.
Table Bloat at 3× — VACUUM Not Running, Queries Degrading Month Over Month
Problem
A high-update SaaS application's core transactions table has grown to 48GB on disk — but pgstattuple analysis shows only 16GB is live data and 32GB is dead tuples from UPDATE and DELETE operations that autovacuum has not reclaimed. Autovacuum is running but cannot keep up with the write volume. Queries that previously ran in 200ms are now taking 2–3 seconds because PostgreSQL must read through large amounts of dead tuple data on the heap pages before finding live rows. Table statistics are stale, causing the query planner to choose inefficient sequential scans.
Solution
Ahex runs VACUUM VERBOSE ANALYZE on the transactions table to reclaim dead tuples immediately, then tunes the autovacuum parameters for high-update tables: autovacuum_vacuum_scale_factor=0.01, autovacuum_analyze_scale_factor=0.005, autovacuum_vacuum_cost_delay=2ms. A pg_repack is scheduled for the largest tables to reclaim disk space without a full table lock. After VACUUM: table size drops from 48GB to 17GB, query times return to 200–250ms baseline, and the autovacuum configuration prevents dead tuple accumulation recurring.
Patroni Failover Taking 4 Minutes — RTO SLA Is 30 Seconds
Problem
An enterprise application has Patroni + etcd configured but has never been tested under realistic conditions. A simulated failover during a maintenance window takes 4 minutes for the standby to be promoted and the application to reconnect — against a 30-second RTO SLA in the disaster recovery plan. Investigation reveals: the application is using individual node connection strings rather than the pgBouncer endpoint, the etcd TTL is set to 120 seconds (the Patroni default), and the standby has a 3-minute replication lag from a heavy bulk load that ran the previous night.
Solution
Ahex reconfigures the Patroni setup — etcd TTL reduced to 10 seconds (ttl: 10, loop_wait: 10), application connection strings updated to use the pgBouncer virtual IP endpoint, synchronous_standby_names configured to prevent the standby falling more than 1 second behind the primary, and pg_rewind enabled for fast standby resynchronisation after a network partition. After reconfiguration: tested failover time drops from 4 minutes to 18 seconds — inside the 30-second RTO SLA. Failover drill is now scheduled quarterly.
PostgreSQL 13 End of Life — No Upgrade Path Identified, Extensions Blocking
Problem
A Python/Django application is running PostgreSQL 13, which reached end of life in November 2025. The security team has flagged the unpatched CVEs. The team wants to upgrade to PostgreSQL 16 but two previous upgrade attempts stalled: the first attempt with pg_upgrade failed because PostGIS 2.5 (installed on PG 13) has no PG 16-compatible package from the distribution repositories; the second attempt using pg_dump/restore took 11 hours on a 900GB database — too long for an acceptable maintenance window.
Solution
Ahex designs a logical replication upgrade path — a new PostgreSQL 16 instance is provisioned with PostGIS 3.4 (PG 16-compatible). A publication is created on the PG 13 source for all tables. A subscription on the PG 16 target replicates the initial data in 4 hours (parallel copy). Sequences and non-replicable objects are migrated with pg_dump --schema-only. Application connections are validated against PG 16 in staging for 2 weeks. Cut-over is a 90-second maintenance window — sequences advanced, publication dropped, application connection strings updated. Zero CVE exposure from day one of cut-over.
Generic Types Misused — Worse Than No Types
Problem
A multi-tenant SaaS application stores all tenant data in a single PostgreSQL database with no schema-level separation. All application queries include a WHERE tenant_id = $1 clause added by the ORM. When a new API endpoint is added without the ORM middleware applied, it returns data from all tenants. Three months in, a bug in the middleware occasionally omits the tenant_id clause — a Tier 1 client can see rows belonging to a different tenant. The security team is alerted. An emergency patch is deployed.
Solution
Ahex implements PostgreSQL Row-Level Security — a policy on every tenant-scoped table: CREATE POLICY tenant_isolation ON orders USING (tenant_id = current_setting('app.current_tenant_id')::uuid). The application sets SET LOCAL app.current_tenant_id = $tenant before each request. RLS is enforced at the PostgreSQL engine level — a missing WHERE clause in the application returns zero rows for the wrong tenant, not all rows. New endpoints cannot bypass RLS without explicitly having BYPASSRLS privilege. The security gap is closed at the database layer, not the application layer.
What We Build Best — Real Expertise, Not Just Slides
Six solution types where our PostgreSQL engineers have deep, repeated delivery experience — every stack listed is what we shipped in production in the last 18 months.
Multi-Tenant SaaS PostgreSQL Architecture
Multi-tenant PostgreSQL schema with Row-Level Security policies, separate schemas per tenant, JSONB for flexible tenant attributes, Patroni HA, pgBouncer connection pooling, and Flyway/Alembic migration pipeline. SPAs with strict tsconfig, generics-first component design, typed state management (NgRx / Zustand), and Zod-validated API layers across the UI.
NestJS / Express Back-End APIs
Fully typed REST and GraphQL APIs with NestJS dependency injection, Prisma typed models, Zod request validation middleware, and tRPC for end-to-end type safety.
Nx / Turborepo Monorepos
Multi-package monorepos with shared @company/types, shared tsconfig bases, ESLint boundary rules, and Nx affected builds that cut CI time by ~60%.
PostgreSQL Major Version Upgrades
Zero-downtime migrations using allowJs incremental strategy, type-coverage audits, any-elimination phases, and strict mode graduation — production stays deployable throughout.
PostGIS Geospatial Applications
AWS Lambda and Vercel PostgreSQL + PostGIS geospatial applications — property search radius queries, logistics route planning, national-scale GIS data ingestion, and Mapbox/Leaflet map integration backed by spatial SQL. Zod-validated payloads, and cold-start optimised bundles under 1MB.
Aurora PostgreSQL Migration
On-premises or RDS PostgreSQL migrated to Amazon Aurora PostgreSQL — DMS or pglogical online migration, compatibility validation, performance baseline comparison, and zero-downtime cut-over with shared types in a monorepo, single CI/CD pipeline, tRPC or OpenAPI contracts, and one team owning the entire stack from DB to UI.
Industry Standards for Enterprise-Grade Compliance & Standards
The following are the industry standards and compliance that we align PostgreSQL with. Our team ensures that these are built into the markup from sprint one only.
Web Content Accessibility Guidelines
WCAG 2.1
AI accessibility scanning flags WCAG violations in real time during development — not post-launch in an audit.
- Required for: Government, Education, Healthcare, Finance
US Federal Accessibility
Section 508
Section 508 for the USA. An U.S. federal accessibility standard that requires government agencies and their digital services to be accessible to people with disabilities.
- Required for: US Government contractors
Americans with Disabilities Act
ADA
A U.S. civil rights law. It promotes the idea that people with disabilities should also have equal access. Its web accessibility requirements encourage businesses to provide inclusive online experiences.
- Required for: All US-facing businesses
Cookie Consent & Data Privacy
GDPR
Standards that help websites collect user data transparently. Supports GDPR and CCPA. Gives users control over their data.
- Required for: EU-facing businesses
W3C PostgreSQL
W3C
W3C PostgreSQL Validation ensures that the PostgreSQL development follows official web standards. It must improve compatibility with browsers, reliability, and overall user experience.
- Best practice for: All websites
Structured Data Markup
Schema.org
Standardized format that helps search engines understand content on the webpages. Improves SEO and crawlability.
- Required for: SEO, Google rich results
Industries We Serve with PostgreSQL
We deliver PostgreSQL database solutions for product companies and enterprises across all major verticals — from healthcare typed APIs to fintech platforms, logistics systems to SaaS products. Click an industry to explore what we've delivered.
Healthcare and Fitness
Our solutions for healthcare and fitness focus on developing user-friendly interfaces for fitness apps, appointment scheduling systems, and health tracking platforms, ensuring secure and efficient data management.
- HIPAA-compliant patient portals
- Cross-device fitness UI experiences
- AI-powered health dashboards
- Real-time telehealth interfaces
Real Estate
We help real estate companies build immersive property listings, interactive maps, and responsive websites that streamline property searches and improve customer engagement.
- GIS-enabled property mapping
- AR/VR property walkthroughs
- CRM-integrated listing portals
- Real-time property analytics
Automotive and Manufacturing
Our front end services help automotive and manufacturing companies build robust applications for managing inventory, tracking production, and enhancing customer engagement through intuitive interfaces.
- IoT-powered monitoring dashboards
- MES-integrated production systems
- Predictive maintenance interfaces
- Supply chain visibility platforms
Banking & Finance
We deliver secure and compliant front-end solutions for financial institutions, enhancing user experience through intuitive dashboards, transaction management systems, and mobile banking apps.
- PCI-DSS compliant interfaces
- AI-driven financial dashboards
- Secure payment interfaces
- Live transaction monitoring UIs
Travel & Hospitality
Our frontend development services for tourism and hospitality focus on creating interactive maps, virtual tours, and streamlined booking interfaces that enhance the customer journey from discovery to booking.
- API-driven booking engines
- Dynamic pricing dashboards
- AI-powered recommendation interfaces
- Multi-channel reservation systems
Entertainment and Media
We help media and entertainment companies build intuitive systems for content delivery and consumption, including real-time single-page applications and personalized content recommendations that keep audiences engaged.
- OTT streaming interfaces
- Real-time content delivery platforms
- Dynamic recommendation experiences
- Live media analytics dashboards
Technology and Software
Our expertise extends to creating modern, scalable front-ends for software applications, ensuring fast performance, intuitive navigation, and seamless integration with backend systems.
- SaaS product dashboards
- Microservices-based frontends
- API-first web interfaces
- Mobile app UIs
Retail & E-Commerce
We empower e-commerce platforms with seamless checkout processes, intuitive product navigation, and responsive designs that boost sales and customer satisfaction.
- Dynamic product recommendation widgets
- Headless commerce frontends
- Omnichannel shopping interfaces
- Real-time inventory dashboards
Education
Our front-end services for education include developing interactive learning platforms, online course management systems, and student portals that enhance engagement and accessibility.
- LMS-integrated learning portals
- AI-based learning dashboards
- Interactive virtual classrooms
- Gamified learning interfaces
Award-Winning Innovation Solutions
Known for building innovative technology solutions across diverse industries, we’ve received multiple awards and recognitions from top B2B platforms.
Clutch 1000 Company – 2025
Recognized by Clutch among the top 1000 global companies for excellence in service and delivery in 2025
Clutch Global Award Winner – Fall 2024
Awarded by Clutch as a Global Leader for outstanding performance and client satisfaction in Fall 2024
Clutch Global Award Winner – Spring 2024
Recognized by Clutch as a Global Leader for delivering high-quality solutions and consistent client success in Spring 2024
Clutch Champion – Fall 2024
Honored by Clutch as a Champion for sustained excellence, industry leadership, and exceptional client feedback in Fall 2024
Clutch Champion – Spring 2024
Honored by Clutch as a Champion for sustained excellence, industry leadership, and exceptional client feedback in Fall 2024
Case Study
Woohoo : Home AI & Enterprise AI Assistant
Woohoo
An enterprise-grade AI Voice Assistant platform designed to automate, govern, and optimize every guest interaction across single and multi-property hotel environments.
- IoT Hub — 1500+ Devices
- Voice-as-a-Service (VaaS)
- 360° Camera + Face ID
- Healthcare & Senior Care
Loan application tracking system
Southwest Funding
Southwest Funding is a Dallas-based mortgage lender founded in 1993 — serving 50,000+ home loans across 29 states with Conventional, FHA, VA, and USDA programs through 1,200+ licensed loan officers.
- Lead-to-Loan Pipeline
- Role-Based Dashboards
- Credit Report Integration
- SMS/Email Notifications
Integral : Development Of An Intuitive Cloud Based EFX Platform
Integral
Integral is a 25+ year Silicon Valley fintech powering institutional-grade FX trading infrastructure — serving banks, brokers, and crypto firms across global markets with cloud-native liquidity aggregation, risk management, and real-time analytics.
- Real-Time Trade Analytics
- Big Data Analysis Engine
- Advanced Chart Modes
- Cloud-Native on AWS
Ready to Optimise Your PostgreSQL Database?
Book a free scoping call with a senior PostgreSQL DBA engineer. We'll review your current PostgreSQL version, data size, application stack, HA architecture, replication setup, query performance pain points, PostGIS or JSONB requirements, and cloud migration goals — and give you an honest assessment of what a query optimisation engagement, Patroni HA setup, or DBA retainer would deliver.
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PostgreSQL Rarely Lives Alone — Pair It With These Services
SQL Server Development
Build secure, scalable, and high-performance applications with SQL Server. Ahex provides database development, performance tuning, high availability, disaster recovery, security, migration, and ongoing database management for demanding workloads.
MySQL Development
Build reliable and scalable database solutions with MySQL, from database design and implementation to performance optimization and cloud deployment. Ahex helps deliver secure, high-performing applications with flexible and cost-effective MySQL solutions.
MongoDB Development
Build flexible and scalable applications with MongoDB's document-oriented database architecture. Ahex provides MongoDB consulting, development, integration, deployment, performance optimization, and dedicated support for distributed and high-volume data environments.
Frequently Asked Question
Yes — it’s the explicit choice of enterprise engineering teams at Use PostgreSQL if your stack is Python/Django, Ruby on Rails, Go, or Node.js with Prisma; if you need JSONB, PostGIS, advanced window functions, CTEs, or superior SQL standards compliance; or if you are deploying on Supabase or Neon. Use MySQL if your stack is PHP/Laravel/WordPress/Magento LAMP, or if you need the broadest simultaneous cloud-managed service support across AWS, GCP, and Azure. For most modern Python and Rails applications, PostgreSQL is the better technical choice. PostgreSQL’s strict mode, shared types, and IDE tooling make large multi-team codebases safe to refactor and extend. For smaller utility scripts plain JavaScript may be fine, but anything long-lived and high-traffic PostgreSQL databases benefit enormously from proper index design and DBA oversight.
Any project with more than one developer, more than a few weeks of lifetime, or pg_stat_statements first — top queries ranked by total_time identify which queries to focus on. EXPLAIN (ANALYZE, BUFFERS) on each query to read the actual execution plan. Index designed for the specific query pattern (composite, partial, or expression index). Index added with CREATE INDEX CONCURRENTLY — no downtime. Performance baseline captured before and after in pg_stat_statements. Prisma, tRPC, and Next.js — it’s the natural choice for the modern JavaScript ecosystem rather than an add-on.
We configure a CI type-check gate (tsc –noEmit) that blocks any PR introducing type errors, activate @typescript-eslint/no-explicit-any and @typescript-eslint/ban-ts-comment to prevent suppressions, and run a type-coverage threshold check on every build. Strictness is enforced by the CI pipeline, not by convention or code review alone.
By default, yes — strict:true enables strictNullChecks, noImplicitAny, strictFunctionTypes, and several other critical checks simultaneously. If you have a legacy codebase where strict mode can’t be enabled immediately, we use an incremental approach — enabling individual flags one at a time and graduating to full strict over sprints.
Typically 3–12 weeks depending on codebase size, existing test coverage, and strictness targets. We use an incremental allowJs strategy — your project stays deployable throughout, never blocked on a big-bang branch. Most production codebases see zero runtime regressions after our migration.
We start with a discovery call to understand your PostgreSQL version, data size, application framework, HA requirements, PostGIS or JSONB requirements, and primary performance pain points. We then propose an engagement model — fixed budget, dedicated team, or time & material — and move into type architecture design, iterative build or migration sprints, and a documented handover with type coverage report.
Absolutely. Yes — we regularly take over PostgreSQL environments from other teams or outgoing DBAs. We start with a full health assessment: pg_stat_statements top query analysis, EXPLAIN ANALYZE on the slowest queries, index usage review, table bloat analysis with pgstattuple, autovacuum effectiveness, replication health check, pgBouncer pool saturation, postgresql.conf audit, and security review against CIS Benchmark. Missing Zod boundaries, and ESLint rule gaps — produce a prioritised remediation roadmap, and execute it incrementally without pausing delivery.
Looking for a Solution? Let's Talk
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