On its own, Postgres is not known for being fast for real-time analytics—but there’s a feature that comes to the rescue: continuous aggregates.

PostgreSQL insights for every developer journey. Get tips and tutorials on database performance, query optimization, time-series data, and more—from real-time analytics and IoT data management to events and vector embeddings. 


TigerData (Creators of TimescaleDB)

Enabling real-time data analytics is a key capability of modern applications, but PostgreSQL materialized views have limitations, such as inefficient refreshes, lack of automatic refreshes, and not showing up-to-date results. Timescale's continuous aggregates solve these limitations by providing automatic and efficient refreshes, as well as combining materialized data with raw data. Continuous aggregates are suitable for real-time analytics, live dashboards, reporting, and more.

Real-time Analytics in Postgres: Why It's Hard (and How to Solve It)

What About Real-Time Analytics? Limitations of PostgreSQL Materialized Views

Meet Continuous Aggregates: Materialized Views With Automatic Refreshes for Real-Time Analytics