Postgres for Everything IRL

Hi friends,?

Lately, we’ve been talking a lot about using Postgres for everything, so in this issue, we decided to let others do the talking. ???

The fantastic team at OpenSauced shares how they are building a “Copilot for git history” using the Postgres extension pgvector and Timescale. Check it out!

Stephan Schmidt’s blog post Just Use Postgres for Everything voiced what we at Timescale have been saying for so long: you don’t need multiple databases to store your demanding workloads or complicated tech stacks. You just need Postgres.?

In a fireside chat with our CTO, Mike Freedman, Stephan shares the reasons behind his love of Postgres, his favorite features (and desired enhancements!), and his perspectives on Postgres and AI, namely vector search. Grab a cup of your favorite beverage and hit play.

See you soon! ??

P.S. In case you missed it, the most clicked link in the previous edition was What We’re Excited About PostgreSQL 17. ??

Hot Off the Press

FEATURED POST

How OpenSauced Is Building a Copilot for Git History With pgvector and Timescale

The OpenSauced team explains how they’re storing metrics on open-source projects and building AI-powered capabilities with a compact but rock-solid tech stack anchored on pgvector and Timescale. Read more

PostgreSQL Data Cleaning vs. Python Data Cleaning

Are you using the best tools for your PostgreSQL data cleaning tasks? Here’s an introduction to some time-saving tools you can use within PostgreSQL itself. Learn more

Understanding Database Workloads: Variable, Bursty, and Uniform Patterns

In this blog post, we explore three primary types of database workloads—variable, bursty, and uniform—and discuss how they impact your database choices. We also offer a better solution for handling all three: Dynamic PostgreSQL. Read more

Time-Series Analysis and Forecasting With Python (Stock Data)

A step-by-step tutorial that will walk you through the different phases of time-series analysis— from data pre-processing to model assessment—using TimescaleDB and Python. Learn more

Time-Series Analysis in R

Want to learn the basics of analyzing time-series data in R? In this blog post, we cover the key concepts and techniques used in time-series analysis, including data exploration, seasonality and trend detection, and forecasting. Read more

Understanding IoT (Internet of Things)

In this blog post, we cover the foundations of one of the most common time-series use cases. We delve into what IoT is, how it works, and its applications in both consumer and industrial settings. Read more

Best Time-Series Databases Compared

A comparison of the leading time-series database products. Plus, a discussion of critical criteria and features to consider when choosing a time-series database. Learn more

THROWBACK

How Percentiles Work (and Why They're Better Than Averages)

Learn what percentile approximations are, how percentiles work in PostgreSQL, and how to calculate them efficiently on time-series data. Read more

BUG FIX RELEASE

TimescaleDB 2.15.1

To ensure our users have an even more stable and reliable database to store their data, our engineers worked on some pesky bugs in the latest TimescaleDB release. The fixes are already available for self-hosted TimescaleDB users and newly created cloud instances, and they will be available next week for existing cloud services during the selected maintenance window.

?Here's a list of everything we improved:?

??? Bugfixes

• #6540: Fix segmentation fault when backfilling data using COPY into a compressed chunk.
• #6858: Fix BEFORE UPDATE trigger not working correctly.
• #6908: Fix time_bucket_gapfill() with timezone behavior around daylight savings time (DST) switches.
• #6911: Fix dropped chunk metadata removal in the update script.
• #6940: Fix pg_upgrade failure by removing regprocedure from the catalog table.
• #6957: Fix then segfault in UNION queries that contain ordering on compressed chunks.

Fireside Chat With Amazing CTO: Why Use PostgreSQL for Everything?!

Join Mike Freedman, Timescale’s co-founder and CTO, and Stephan Schmidt, CTO coach, in this chat about PostgreSQL and why you should use it for everything.

Tips From the Tiger Trenches

Sharing what our community reads, tweets, watches, or listens to!

Thanks to the Mave team for trusting TimescaleDB to improve their platform’s privacy and performance! They explain everything in this cool blog post.

Timescale Careers

Team Timescale is growing! If you know someone who'd like to join our team—or learn more about life on Team Timescale ??—we're currently hiring (100 percent remote).?

?? Check out our careers page to view all of our open positions.?