The Power of Linked Data Event Streams and Timescale for Real-Time Management of Time-Series Data

Learn why data locality can be crucial to downsampling data faster and more efficiently, accelerating the work of both devs and businesses. To test this new approach, we benchmarked the LTTB downsampling algorithm in Ruby and compared it with the Timescale Toolkit lttb ().
Join us in celebrating 12 highlights of 2022 (and then some), including launches like one-click forking and replicas, our consumption-based, bottomless object store in Timescale, and the return of in-person events (great to see you!).
TimescaleDB expands PostgreSQL query performance by 1,000x, reduces storage utilization by 90%, and provides time-saving features for time-series and analytical applications—while still being 100% Postgres.
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).
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).
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.
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.
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.
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.