Tiger Data (creators of TimescaleDB)

The Postgres write path was designed for data that lives in flux: rows that get updated, deleted, and read in any order by concurrent transactions. Append-only telemetry data doesn't work that way. A row arrives, gets inserted, and is never touched again. Weeks later, an entire partition gets dropped, not an individual row. But every one of those rows still carries a 23-byte MVCC header for a transaction nobody will ever contest. Every row pays for torn-page protection on a page that will never be rewritten. Every row gets its own B-tree index entry for a lookup pattern that only ever runs as a time-range scan. You're paying for guarantees your data never exercises. Tuning can get you to about 2.5x write amplification. It can't fix a mismatch between the write path and the workload. Past the structural floor, that's not a config problem. Matty Stratton names the mismatch and what changes once you do. https://tsdb.co/amp-tax-li #PostgreSQL #TimescaleDB #DatabasePerformance #TimeSeriesDatabase

If you ship on Postgres, you've probably hit at least one of these. In this edition, we tackle four practical headaches developers run into in production, and what to do about each: write costs you didn't budget for, autovacuum running on tables nobody updates, schemas that crush dashboards once they grow, and how Postgres extensions can quietly replace half your stack. ▪ The 3-4x tax every Postgres insert quietly pays  ▪ The autovacuum tax append-only tables keep paying  ▪ A cheat sheet for replacing 7 specialized databases with SQL  ▪ Why relational complexity crushes real-time dashboards past 500M rows Plus a free hands-on workshop on May 28 where you'll build an IoT analytics pipeline from scratch. Full edition: https://lnkd.in/ebUGZ9UA

Live from AWS Summit Hamburg! 🗣️ We’re at booth S03 talking with teams building large-scale IoT and real-time applications on AWS. Tiger Data extends Postgres with time-series primitives and columnar storage so you can run analytics directly on live operational data without adding another system, pipeline, or warehouse. The team will run live demos and hold 1:1 meetings throughout the day. Come say hi! https://tsdb.co/3iv7fphb #AWS #Hamburg #Postgres #Timescale

We're at booth S03, let's talk about scaling your IoT workloads at AWS Summit Hamburg #aws #hamburg #timescale #postgres #tigerdata

What a quarter. Had ~2x expected revenue growth, our largest net revenue growth month ever in April, and accelerating at scale. The most exciting part is what's driving it: industrial, manufacturing, energy, robotics, and datacenters. AI isn't just changing software. It's driving a re-industrialization wave, and that wave needs modern data infrastructure for machines, sensors, operations, and real-time intelligence. We're seeing it directly in our users. The physical world is coming online, and it runs on data.

Last day at IoT Tech! 🚀 The team is at booth 104 and ready to chat all things databases, time-series, and building faster with modern data infrastructure. Stop by, say hi, and let’s talk data! https://tsdb.co/grrztwhz

Very happy that Tiger Data (creators of TimescaleDB) can financially support the continued development of pgBackRest. https://lnkd.in/eVCrhFvn

Very happy that Tiger Data (creators of TimescaleDB) can financially support the continued development of pgBackRest. https://lnkd.in/eVCrhFvn

10 minutes until this session begins: 📍 12:50-1:00 PM Escaping IIoT Pilot Purgatory: Why Your Database Breaks at Scale Matty Stratton, Head of Developer Advocacy and Docs at Tiger Data (creators of TimescaleDB), will dive into why so many IIoT projects fail to scale - and how to avoid hitting the same wall. Key insights include: • Why 74% of manufacturers get stuck in pilot mode • Hidden scaling limits in storage, ingest, and query performance • How database bottlenecks impact real-time operations • Architectural strategies that actually enable production-scale systems A practical session for anyone looking to move beyond proof of concept and build IIoT systems that perform at scale. #IIoT #DataEngineering #EdgeComputing #IoT #Scalability #DigitalTransformation