Tiger Data - Creators of TimescaleDB

🐯 @TimescaleDB is now TigerData! 🚀 When we launched Timescale, the top Hacker News comment said it was “a bad idea.” PostgreSQL wasn’t supposed to be fast. Or scalable. Or useful for time-series. 8 years later: -2,000 customers -8-digit ARR -Most workloads aren’t even time-series anymore We’ve changed our name to reflect that evolution: Timescale is now TigerData. Same code. Same team. Still PostgreSQL. Just a lot faster.

Float collects energy data every second. The Danish grid settles every 15 min. Continuous Aggregates bridge the gap: no batch jobs, no pipelines. 99.3% compression keeps it viable. tsdb.co/bjgkrzh1 @floatenergy #TimescaleDB

Postgres handles 90% of workloads well. The other 10%: append-only writes, sustained ingest, time-range aggregations. If all three apply, you're not in a tuning problem. You're in an architecture problem. One query confirms it: tsdb.co/postgresql-fit-x

The focus of our June 17 newsletter: doing less unnecessary work on every layer of the stack. In this edition: ▪ TimescaleDB 2.27: bloom filter pruning extends to UPDATE, DELETE, and UPSERT, plus 30% to 2x faster vectorized execution ▪ When PostgreSQL isn't the right fit (and the one pg_stat_user_tables query that tells you which workload you have) ▪ Why analytical scans read 16x more data than they need to ▪ How a Unified Namespace determines your historian schema before you write a CREATE TABLE ▪ New Kepware and Fivetran integration guides Plus four June events: a CERN webinar, our team at Data Driven Oil & Gas in Houston, the FIFA World Cup watch party that same night, and a hands-on Sensor-to-Insight workshop June 30. Full edition:linkedin.com/pulse/tiger-dat… #PostgreSQL #TimescaleDB #IIoT #DataEngineering

At Hannover Messe 2026, Tiger Data co-founder and CTO Mike Freedman sat with @IIoT_World co-founder Lucian Fogoros to break down how @TimescaleDB runs 10x faster than SQL Server, Oracle, or MongoDB at a fraction of the footprint. They also covered the formal partnership with Inductive Automation after seven years of organic Ignition Community adoption, and the launch of TimescaleDB Enterprise for facilities where data cannot leave the building. Full interview on YouTube: youtu.be/p1sUI-qVfU4

TimescaleDB will be at #AWSSummit Hamburg. Let’s talk PostgreSQL, real-time analytics, time-series workloads on AWS, and AI applications built on operational data. Book a meeting with our team at the event! tsdb.co/i4nemls3

Postgres internals: your reads are writing to disk. When Postgres reads a new row, it sets a hint bit in the tuple header. That write dirties the page. I/O from reads on "immutable" data. New post: tsdb.co/autovacuum-tax-x #PostgreSQL

Meet the TimescaleDB team at #IoTTechExpo, May 18–19 at Booth #104. We’ll be talking real-time analytics, IoT infrastructure, PostgreSQL, and AI-powered applications built on operational data. Book a meeting with our team at the event. tsdb.co/3gr6qfe5

Teams call it a database migration. That's the wrong mental model. TimescaleDB is a Postgres extension. Same SQL. Same drivers. Same wire protocol. You're not leaving Postgres. You're giving it better tools. tsdb.co/postgres-migration-x

Postgres write amplification has a floor you cannot tune below. WAL writes every byte twice (log, then data file). Structural 2x. Aggressive tuning gets you to ~2.5x. Anyone quoting less is measuring something else. tsdb.co/amp-tax-x

50 parallel queries. 1M log records. TimescaleDB: 6.2miliseconds vs ClickHouse: 6,307miliseconds A dev built a full observability benchmark (logs, spans, metrics) across 54 tests. TimescaleDB won 96%. Full results: builder.aws.com/content/3Aor…

👥 Live at @SensorsConverge today. Sensor data outgrows your database. The usual fix is a second system, with all the pipelines that brings. Skip the split. Tiger Data extends Postgres. Booth 1342 until 5:30. tsdb.co/sensors-2026-x #SensorsConverge

@flightaware: 6.4s to 30ms. No ClickHouse. No pipeline. Still on Postgres. Before you build a pipeline to feed a second database, worth asking if the problem is the database or the primitives. tsdb.co/jqva64wh

We'll be at @SensorsConverge, May 5-7. Booth 1342, Santa Clara. If you're building IoT systems and hitting performance walls as sensor data scales, come talk to us. Book a time: tsdb.co/sensors-2026-x

Big news: we’re partnering with @InductiveAuto. A modern, SQL-native foundation for industrial data - built for scale, speed, and real-time insight. Read more: inductiveautomation.com/news…

We will be at #OTC2026 in Houston, booth #3810. If your team runs on high-frequency sensor or SCADA data, come find us. Book a meeting: ow.ly/xrsU50YRYCH #OilAndGas #PostgreSQL

Autovacuum running constantly but n_dead_tup keeps climbing? That's not a config problem. That's an architecture problem. Stop generating dead tuples. Use partition-based retention, not row deletes. ow.ly/LI7b50YRYjS

6 to 8 seconds on vanilla Postgres. Under 100ms on TimescaleDB. Solo dev Ankur Podder built an interactive map showing whether a concrete plant is actually decarbonizing, or the grid just got cleaner. Latest in our Community Spotlight series: tsdb.co/carbon-observatory-x

We're live at AWS Summit London. Stop by and find the Tiger Data team - we're talking Postgres at scale, time-series, and what it takes to build high-performance data applications on AWS. tsdb.co/bwx9p341

TimescaleDB's whole idea: start on Postgres, keep scaling on Postgres. TimescaleDB 2.26: summary queries on compressed hypertables now resolve from chunk metadata instead of decompressing rows. 940ms to 13ms in our benchmark. Plus 3.5x faster time_bucket() aggregations and 2x+ multi-column lookups. tsdb.co/a5swiekg #TimescaleDB #PostgreSQL