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.

AI capacity problems get treated as five separate crises: GPUs, memory, cooling, power, water. They're one problem, and they hit in a fixed order.

Recording of TJ's great presentation about pg_textsearch's design and implementation at recent pgconf dev. Link to Youtube video and GitHub repo below.

Intelligence isn't the bottleneck in robotics. Operational data infrastructure is. Robots can reason. But reliability at scale requires learning from millions of real-world cycles. That data problem has no internet shortcut. Click to read: automationworld.com/factory/roboti…

Most industrial orgs are drowning in data they can't actually analyze in real-time. Traditional databases + legacy historians = architecture that worked in 2015, not today. That's why modern time-series databases matter. Stay operational, stay analytical, stay on Postgres. No splits. No lag. Just data that works. linkedin.com/posts/timescal…

Great to see a full room for @michaelfreedman's session, From Asset to Fleet: Solar and BESS Operations with Ignition and TimescaleDB, at Solar & BESS Discovery Day. As solar and battery storage operations scale, so do the data challenges. Mike shared how teams are building scalable data architectures to move from asset-level monitoring to fleet-wide visibility and real-time analytics. Thanks to @InductiveAuto for hosting a great event and everyone who joined the discussion!

Real-time analytics can unlock new ways to monitor complex systems. This case study by @TigerDatabase explores how data compression helped enable real-time energy tracking at scale: hackernoon.com/how-float-runs…

TimescaleDB just hit 10,000 contributions. Every ticket, comment, and PR got us here, from Tiger Data engineers to contributors around the world. Thank you. On to 100,000. #TimescaleDB #PostgreSQL #OpenSource

25 years in building automation. Zero production TypeScript. Now runs a SaaS rendering 12 months of energy data across 38 buildings in under a second. Telemetry + materialized views + pgvector, all in one Postgres. Full community spotlight: tsdb.co/apexanalytica-x

One governed TimescaleDB table. Three protocols (Modbus, OPC UA, MQTT). Zero middleware. Claude Code Agent Teams build the collection layer and enforce the UNS naming contract while the code is written, not reconciled after. Walkthrough + failure path: tsdb.co/plant-floor-cl…

The clearest signal from ten events this spring: fewer people asking "what is a time-series database" and more asking about compression ratios and Hypercore under high-cardinality dashboards. The market moved past category education. tsdb.co/spring-events-x #Postgres #TimeSeries

A tiny 10,000-row Postgres table could be secretly slowing down every of your dashboard query. Learn how to fix it in minutes without a migration with this guide by @TigerDatabase:

A huge thank you to all the sponsors of the upcoming Solar & BESS Discovery Day! EXECUTIVE: @Advantech_USA, @RRCcompanies, @RoviSys, @opto22, @MoxaInc WELCOME RECEPTION: @CirrusLink, @TigerDatabase, @TeamVertech MEDIA: EnergyTech Podcast Learn more: bit.ly/4vEu7zB

So fun to share the @TigerDatabase story with Noah and the folks at Code Story. Thanks for having me! 🐯🚀 codestory.co/podcast/bonus-…

⚡@TimescaleDB 2.27 is out. For 10 years, we've had a consistent vision: start with Postgres, scale with Postgres. Reduce the need for complex data stacks with lagging CDC pipelines, weaker consistency, stale data, and more operational surface area. This release continues that work by making Hypercore, our Postgres-native columnstore, faster across more workloads. Specifically: • Selects. More filters now run vectorized through the standard Postgres function path, including in continuous aggregate refreshes. 30% - 200% improvements. • Updates and deletes. Bloom filters can skip decompressing compressed batches that cannot match equality predicates. Some crazy improvements up to 160x. • Upserts. Bloom filters can also prune batches during arbiter checks, avoiding unnecessary decompression on conflict-heavy write paths. Same Postgres. Less unnecessary work. Faster at scale.

For years, compute treated power as something to optimize. Now power is becoming the constraint. The more time I spend around AI data center projects, the more it’s clear this has shifted from a facilities issue into a complex systems problem. A lot of my academic career was spent focusing on distributed systems, networking, and storage, thinking about topics like server utilization, network bisection, storage placement, scheduling, and cluster orchestration. We all worked under the assumption that physical infrastructure was simply a given foundation for the compute layer. There was a wave of systems work on green computing in the late 2000s, especially around energy-proportional computing, power-aware systems, and data center efficiency. But power was still treated as an optimization variable, and what feels different now is that power itself is becoming the constraint. While AI infrastructure conversations usually focus on chips and clusters, the constraints increasingly show up in the physical infrastructure: power, cooling, water, grid interconnects, backup generation, and the ability to operate dense compute reliably. You can see it at the market level too. The proposed $67B deal between NextEra and Dominion deal is being seen explicitly as a direct response to the massive electricity requirements of AI data centers. The demands are enough to change the strategic logic of the power sector itself. At @TimescaleDB, we are experiencing this firsthand. We have seen hyperscalers deny server expansion requests not due to a lack of hardware or demand, but because of regional power capacity limits. The physical plant is now an inseparable part of the broader systems architecture. The compute layer and the physical infrastructure layer are more tightly coupled than most software people are used to thinking about. Power, cooling, water, capacity, utilization, and equipment state are no longer just metrics in an operations dashboard. They become part of how operators understand the facility, plan growth, investigate failures, improve efficiency, and decide where compute can actually run. This is also becoming a real-time systems problem. As power density and heat density increase, the operational control loop becomes more important. The system has to respond to changing thermal conditions, workload placement, cooling behavior, and infrastructure state. The shift from primarily air-cooled environments toward more liquid-cooled designs only makes that coupling tighter. Ultimately, the "AI factory" is as much an energy and infrastructure challenge as it is a compute one. Because the physical system now defines the digital performance envelope, the operational data layer has moved from the periphery to the center of system architecture. More to write.

Now let's undo the undo with TigerFS to restore the changes... Filesystem interface + skills. Agents are so good at this.

PSA for #Postgres extension developers: Consider adding open-source pgspot to your release CI pipeline. github.com/timescale/pgsp… We regularly evaluate extensions for Tiger Cloud, and pgspot consistently finds security issues, privilege escalations, and unsafe patterns before deployment. We try to report what we find upstream through issues and PRs, but even better is catching these problems before a release ever ships.

Continuous aggregates are one of the most popular TimescaleDB features: incrementally maintained rollups that accelerate analytical queries while transparently handling late-arriving and backfilled data. The challenge is that analytical questions evolve. Need another aggregation? Historically, that meant dropping and rebuilding the continuous aggregate. In @TimescaleDB 2.28, adding a new aggregate to an existing continuous aggregate is just: > ALTER MATERIALIZED VIEW ... ADD COLUMN New values are computed automatically going forward. If you want historical values for the new column, simply run a refresh. Analytical requirements change. Your rollups should be able to change with them.

10,000 GitHub contributions for @TimescaleDB! That's a metric to celebrate! 🥳

Close Full details in Brandon's write-up. tsdb.co/8r1vpily Tiger Data (creators of TimescaleDB) #PostgreSQL #databases