






PostgreSQL + TimescaleDB: 1,000x Faster Queries, 90 % Data Compression, and Much More
TimescaleDB expands PostgreSQL query performance by 1000x, reduces storage utilization by 90%, and provides time-saving features for time-series and analytical applications—while still being 100% Postgres.





How We Made Data Aggregation Better and Faster on PostgreSQL With TimescaleDB 2.7
They’re so fast we can’t catch up! Check out our benchmarks with two datasets to learn how we used continuous aggregates to make queries up to 44,000x faster, while requiring 60 % less storage (on average).

Increase Your Storage Savings With TimescaleDB 2.6: Introducing Compression for Continuous Aggregates
Timescale 2.6 is now available, introducing two highly requested features by our community: compression for continuous aggregates and timezone support for continuous aggregates (the latter under experimental).



Timescale flies when you’re having fun: 2021 in review
We're celebrating the accomplishments that made us proud in 2021 - like the advances in Timescale Cloud and TimescaleDB, new hyperfunctions, function pipelines, the support for OpenTelemetry in Promscale, and the many other launches from the year.




PostgreSQL vs Python for Data Evaluation: What, Why, and How
Get a primer on using TimescaleDB and PostgreSQL to more efficiently perform your data evaluation tasks - previously done in Excel, R, or Python. Complete with short SQL refresher section, along with 1-to-1 code snippets comparing TimescaleDB and PostgreSQL code against Python code.

AWS Lambda for Beginners: Overcoming the Most Common Challenges
Learn how to overcome three common challenges while building data pipelines with AWS Lambda. Add external dependencies to your Lambda function with Layers, overcome the 250MB package limit with Docker, and set up continuous deployment with GitHub Actions.

Speeding up data analysis with TimescaleDB and PostgreSQL
Is your data analysis process as fast and efficient as it could be? This four-part blog series will outline common data analysis problems and how TimescaleDB and PostgreSQL fixed them by making data munging tasks within analysis fast, efficient, and easily accessible.

How to create (lots!) of sample time-series data with PostgreSQL generate_series()
Generating sample time-series data with the PostgreSQL generate_series() function is a useful skill to have when evaluating new database features, creating demonstrations, or testing insert and query patterns. Learn what PostgreSQL `generate_series()` is and how to use it for basic data generation.

Hacking NFL data with PostgreSQL, TimescaleDB, and SQL
Learn how to use time-series data provided by the NFL to uncover valuable insights into many player performance metrics – and ways to apply the same methods to improve your fantasy league team, or your knowledge of the game - all with PostgreSQL, SQL, and freely available extensions.
