General
Downsampling in the Database: How Data Locality Can Improve Data Analysis
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 ().
General
Year of the Tiger: 12 (and Then Some) Timescale Highlights of 2022
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!).
PostgreSQL
PostgreSQL + TimescaleDB: 1,000x Faster Queries, 90 % Data Compression, and Much More
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.
Engineering
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).
General
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).
General
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.
General
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.