Always Be Launching
Massive scale for time-series workloads: Introducing continuous aggregates for distributed hypertables in TimescaleDB 2.5
With this release, we introduce continuous aggregates in multi-node TimescaleDB, support for Postgres 14, and support for time zones in time_bucket_ng (under experimental).
Blog
What is ClickHouse, how does it compare to PostgreSQL and TimescaleDB, and how does it perform for time-series data?
An in-depth look at how ClickHouse and TimescaleDB architectures impact application design, developer experience, and performance - with detailed benchmark results comparing ingest speeds, disk space, and query response times.
Always Be Launching
Function pipelines: Building functional programming into PostgreSQL using custom operators
Today we are releasing function pipelines, a new feature that allows you to analyze data by composing multiple functions in SQL - introducing a simpler, cleaner way of expressing complex logic in PostgreSQL.
Blog
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.
PostgreSQL
How percentile approximation works (and why it's more useful than averages)
Get a primer on percentile approximations, why they're useful for analyzing large time-series data sets, and how we created the percentile approximation hyperfunctions to be efficient to compute, parallelizable, and useful with continuous aggregates and other advanced TimescaleDB features.
PostgreSQL
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.
PostgreSQL
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.
Observability
Simplified Prometheus monitoring for your entire organization with Promscale
We’re excited to announce multi-tenancy support in Promscale. We’ve built Promscale as a centralized store for metrics, and now developers can enjoy the same operational maturity and query flexibility when storing metrics across their entire organization.
Product & Engineering
How PostgreSQL aggregation works and how it inspired our hyperfunctions’ design
Get a primer on PostgreSQL aggregation, how PostgreSQL’s implementation inspired us as we built TimescaleDB hyperfunctions and its integrations with advanced TimescaleDB features – and what this means for developers.
Announcements & Releases
Move fast, but don’t break things: Introducing the experimental schema (with new experimental features) in TimescaleDB 2.4
To reinforce our commitment to moving fast and not breaking things, we are introducing a new experimental schema for TimescaleDB as part of our release of TimescaleDB 2.4. Read on for more about how Timescale resolves the tension between rapid innovation and stability.
General
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.
