PostgreSQL performance issues don’t always require scaling hardware.

Tomorrow, Stormatics & DBtune are hosting a webinar on “Achieving 80% PostgreSQL Performance Gains Without Scaling Up.”

Semab Tariq and Mohsin Ejaz will share real tuning work that cut query times to seconds, reduced CPU usage by 70%, stabilized memory, and improved batch workloads covering query plan analysis, AI-assisted parameter tuning, indexing, and PostgreSQL internals.

Register: https://resources.stormatics.tech/postgresql-performance-gains-without-scaling-up

Whop migrated PostgreSQL from Heroku to AWS after their Series A to unlock scalability. Heroku lagged on PostgreSQL versions, limited extensions, and didn’t support external replication—making migration harder. Stormatics built a WAL log shipping path, completed cutover in under 10 minutes, and deployed a 3-node HA cluster with repmgr plus Barman backups and PITR.

Read the case study: https://resources.stormatics.tech/whop-database-infrastructure-migration

Replication lag often appears when PostgreSQL workloads move from single-region to distributed setups. What looks stable locally can behave very differently once network latency and cross-region coordination are involved.

Our whitepaper benchmarks replication lag in single-region vs multi-region PostgreSQL architectures using pgbench and HammerDB, with insights and recommendations for running distributed clusters.

Download the whitepaper: https://resources.stormatics.tech/benchmark-study-on-replication-lag-in-postgresql

Can PostgreSQL performance only be improved by scaling hardware? Often that just hides real bottlenecks and raises costs.

Join our webinar with DBtune on 17 March 2026 at 3 PM GMT. Semab Tariq (Stormatics) and Mohsin Ejaz (DBtune) will share real examples of achieving up to 80% average performance improvement through targeted PostgreSQL tuning—without changing infrastructure.

Register: https://resources.stormatics.tech/postgresql-performance-gains-without-scaling-up

ORM-driven applications often slow down for reasons that cannot be fixed in application code. Since the ORM controls query shape, indexing becomes the main performance lever. In this post, Hamza explains how he improved performance by up to 80 percent using strategic PostgreSQL indexing, targeting heavy sequential scans and missing foreign key indexes without modifying queries.

Full breakdown:
https://stormatics.tech/blogs/fixing-orm-slowness-by-80-with-strategic-postgresql-indexing

PostgreSQL performance issues don’t happen overnight. As traffic grows, slow queries, indexing gaps, autovacuum lag, and outdated memory settings quietly add up, until users feel the latency.

Our whitepaper helps technical teams identify root causes, improve indexing and partitioning, tune autovacuum and memory, and leverage parallel execution and advanced optimization tools.

Download the whitepaper: https://resources.stormatics.tech/high-performance-postgresql-practical-guide-to-optimization

Last chance to register!

Join Warda Bibi, PostgreSQL Consultant at Stormatics, tomorrow (25 Feb, 3PM GMT) for a live session on “PostgreSQL Query Optimization: How to Read EXPLAIN ANALYZE” with a hands-on demo.

Learn the difference between EXPLAIN and EXPLAIN ANALYZE, understand planner vs executor, read execution plans step by step, identify bottlenecks, and apply practical optimization techniques.

Register now: https://resources.stormatics.tech/postgresql-query-optimization

High availability is non-negotiable for mission-critical PostgreSQL. Pgpool strengthens Postgres with connection pooling, load balancing, query caching, and automatic failover, while pg_cirrus enables consistent, production-ready HA cluster deployments. Semab Tariq (Stormatics) and Muhammad Usama (Microsoft) demonstrated how to build a resilient PostgreSQL HA cluster and shared best practices for eliminating single points of failure.

Watch the recording: https://resources.stormatics.tech/high-availability-in-postgres-with-pgpool-pg_cirrus

When expensive joins and aggregations power every dashboard load, the cost compounds. At scale, it shows up as timeouts, unstable latency, and unnecessary I/O. Materialized views change the shape of that problem by persisting the result and refreshing it on a defined schedule. Fast reads, bounded staleness, controlled refresh cost.

This post covers when they fit, how they compare to views and summary tables, and how to handle indexing and refresh strategy:
Read now: https://stormatics.tech/blogs/postgresql-materialized-views-when-caching-your-query-results-makes-sense