PostgreSQL powers analytics platforms with its advanced aggregation capabilities, window functions, CTEs, and materialized views. Combined with extensions like TimescaleDB (time-series), pg_partman (partitioning), and Citus (distributed), PostgreSQL scales from startup analytics...
ZTABS builds analytics platforms with PostgreSQL — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. PostgreSQL powers analytics platforms with its advanced aggregation capabilities, window functions, CTEs, and materialized views. Combined with extensions like TimescaleDB (time-series), pg_partman (partitioning), and Citus (distributed), PostgreSQL scales from startup analytics to enterprise data warehousing. Get a free consultation →
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Projects Delivered
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Years Experience
PostgreSQL is a proven choice for analytics platforms. Our team has delivered hundreds of analytics platforms projects with PostgreSQL, and the results speak for themselves.
PostgreSQL powers analytics platforms with its advanced aggregation capabilities, window functions, CTEs, and materialized views. Combined with extensions like TimescaleDB (time-series), pg_partman (partitioning), and Citus (distributed), PostgreSQL scales from startup analytics to enterprise data warehousing. For teams that want a single database for both transactional and analytical workloads (HTAP), PostgreSQL avoids the complexity and cost of maintaining separate OLTP and OLAP systems.
Window functions, CTEs, LATERAL joins, and GROUPING SETS handle complex analytical queries that MySQL and MongoDB cannot express in a single query.
Pre-compute expensive aggregate queries as materialized views. Dashboards read pre-calculated data in milliseconds instead of running heavy queries.
TimescaleDB extension adds automatic partitioning, compression, and continuous aggregates for time-series data. 20-100x faster than vanilla PostgreSQL for time-series.
Scale PostgreSQL horizontally across multiple nodes with Citus. Handle petabyte-scale analytics with the same PostgreSQL SQL you already know.
Building analytics platforms with PostgreSQL?
Our team has delivered hundreds of PostgreSQL projects. Talk to a senior engineer today.
Schedule a CallUse materialized views with CONCURRENTLY refresh to pre-compute dashboard data. The CONCURRENTLY flag avoids locking the view during refresh, keeping dashboards available 24/7.
PostgreSQL has become the go-to choice for analytics platforms because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Database | PostgreSQL 16 + TimescaleDB |
| Distribution | Citus (optional) |
| Visualization | Metabase / Grafana / Superset |
| ETL | dbt / Airflow |
| Cache | Materialized views + Redis |
| Hosting | Neon / Timescale Cloud / RDS |
A PostgreSQL analytics platform ingests data through ETL pipelines (dbt transformations running in Airflow). Raw data is partitioned by date range using pg_partman for efficient querying of time-bounded data. Materialized views pre-compute common analytics — daily revenue, user cohort metrics, funnel conversion rates — and refresh on schedule.
Dashboards read from materialized views for instant rendering. For ad-hoc analysis, window functions calculate running totals, moving averages, and rankings. CTEs structure complex multi-step queries readably.
TimescaleDB extension handles time-series metrics (server monitoring, IoT sensor data, financial ticks) with automatic chunk partitioning, compression (95% storage reduction), and continuous aggregates (real-time materialized views).
Our senior PostgreSQL engineers have delivered 500+ projects. Get a free consultation with a technical architect.