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🐯 Timescale is now TigerData! 🚀
Eight years ago, we started as a PostgreSQL-based time-series database. But innovation never stands still—and neither have we.
Today, we proudly unveil @TigerDatabase, marking our evolution into the fastest and most powerful PostgreSQL for modern transactional, analytical, and AI-driven workloads.
🌟 Why TigerData?
We’ve grown far beyond our initial identity. From handling early renewable energy use-cases to powering Bloomberg’s critical geotemporal data, our customers showed us we're not just the best in time-series—we're the best in PostgreSQL, period.
🔑 Our Journey Highlights:
- 2017: TimescaleDB introduces groundbreaking hypertables.
- 2019: Native columnar compression; our cloud service launches.
- Recent innovations: 2500x faster distinct queries, 8x more efficient Boolean storage, and ultra-fast queries on high-cardinality data.
🚀 Customer Impact:
- Bloomberg leveraged us for massive geotemporal datasets.
- Renewable energy utilities replaced Redis to efficiently monitor operations.
🤝 Community Commitment:
Our core remains open-source, including the beloved TimescaleDB extension. This rebrand changes nothing about our dedication to our community and customers.
🌐 Looking Ahead:
Exciting innovations await:
- Deeper lakehouse integrations.
- AI-native features.
- Enhanced scalability and unmatched performance.
We are not just renaming; we're recommitting—to deliver on our mission of building the fastest Postgres, and to give our customers speed without sacrifice.
✨ Join the Tiger Era! 🐯
Your feedback is invaluable. Tell us your thoughts and what you'd love to see next.
Ready for the ride? Let's roar ahead together!
tsdb.co/tigerdata
Eight years ago, we started as a PostgreSQL-based time-series database. But innovation never stands still—and neither have we.
Today, we proudly unveil @TigerDatabase, marking our evolution into the fastest and most powerful PostgreSQL for modern transactional, analytical, and AI-driven workloads.
🌟 Why TigerData?
We’ve grown far beyond our initial identity. From handling early renewable energy use-cases to powering Bloomberg’s critical geotemporal data, our customers showed us we're not just the best in time-series—we're the best in PostgreSQL, period.
🔑 Our Journey Highlights:
- 2017: TimescaleDB introduces groundbreaking hypertables.
- 2019: Native columnar compression; our cloud service launches.
- Recent innovations: 2500x faster distinct queries, 8x more efficient Boolean storage, and ultra-fast queries on high-cardinality data.
🚀 Customer Impact:
- Bloomberg leveraged us for massive geotemporal datasets.
- Renewable energy utilities replaced Redis to efficiently monitor operations.
🤝 Community Commitment:
Our core remains open-source, including the beloved TimescaleDB extension. This rebrand changes nothing about our dedication to our community and customers.
🌐 Looking Ahead:
Exciting innovations await:
- Deeper lakehouse integrations.
- AI-native features.
- Enhanced scalability and unmatched performance.
We are not just renaming; we're recommitting—to deliver on our mission of building the fastest Postgres, and to give our customers speed without sacrifice.
✨ Join the Tiger Era! 🐯
Your feedback is invaluable. Tell us your thoughts and what you'd love to see next.
Ready for the ride? Let's roar ahead together!
tsdb.co/tigerdata
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TimescaleDB Enterprise is now in public beta.
High-performance TimescaleDB and PostgreSQL on infrastructure you control, with an integrated control plane for HA, backups, monitoring and upgrades.
Get started: hubs.li/Q04Dl7fL0
High-performance TimescaleDB and PostgreSQL on infrastructure you control, with an integrated control plane for HA, backups, monitoring and upgrades.
Get started: hubs.li/Q04Dl7fL0
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TimescaleDB (by Tiger Data) retweeted
Today we're announcing the public beta of @TimescaleDB Enterprise!
We're in the midst of a massive industrial buildout. New factories, expanding energy infrastructure, AI data centers, more capable robotics, and existing facilities being modernized and retooled.
But it's not just about building more. It's about building smarter. Operators want greater visibility, tighter control, and better ways to optimize operations, including AI agents that help investigate problems and recommend actions. That means more sensors, more software, and a lot more data.
We think it's time to rethink the industrial historian. Not just a database for storing tag history, but the data foundation for a new generation of industrial applications.
We've built TimescaleDB Enterprise around three priorities:
1. Scale & performance. High-speed ingestion, efficient columnar compression and storage, and fast analytical queries over massive volumes of time-series data, all on PostgreSQL.
2. Enterprise-grade reliability & management. High availability, disaster recovery, monitoring, and upgrades. The capabilities needed to operate mission-critical databases on infrastructure you control.
3. Manage the entire data architecture. Operate independent databases across edge, site, and enterprise, and control how data moves between them. Keep detailed history local while sharing data, aggregates, and context where they’re needed.
TimescaleDB Enterprise also provides the database foundation for the new Ignition Enterprise Historian, building on our partnership with Inductive Automation. We're launching the public beta alongside Ignition 2027 today.
And it's not just for industrial use cases. If you're running TimescaleDB on your own infra, we'd love for you to try it out.
tigerdata.com/blog/timescale…
We're in the midst of a massive industrial buildout. New factories, expanding energy infrastructure, AI data centers, more capable robotics, and existing facilities being modernized and retooled.
But it's not just about building more. It's about building smarter. Operators want greater visibility, tighter control, and better ways to optimize operations, including AI agents that help investigate problems and recommend actions. That means more sensors, more software, and a lot more data.
We think it's time to rethink the industrial historian. Not just a database for storing tag history, but the data foundation for a new generation of industrial applications.
We've built TimescaleDB Enterprise around three priorities:
1. Scale & performance. High-speed ingestion, efficient columnar compression and storage, and fast analytical queries over massive volumes of time-series data, all on PostgreSQL.
2. Enterprise-grade reliability & management. High availability, disaster recovery, monitoring, and upgrades. The capabilities needed to operate mission-critical databases on infrastructure you control.
3. Manage the entire data architecture. Operate independent databases across edge, site, and enterprise, and control how data moves between them. Keep detailed history local while sharing data, aggregates, and context where they’re needed.
TimescaleDB Enterprise also provides the database foundation for the new Ignition Enterprise Historian, building on our partnership with Inductive Automation. We're launching the public beta alongside Ignition 2027 today.
And it's not just for industrial use cases. If you're running TimescaleDB on your own infra, we'd love for you to try it out.
tigerdata.com/blog/timescale…
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Heading to #EnlitEurope? ⚡🇦🇹
Let's talk SCADA, smart meter data, and real-time analytics on Postgres!
📅 Nov 10–12
📍 USA Pavilion, Stand A.d117
Come meet the Tiger Data team!
👉 tigerdata.com/events/enlit-e…
Let's talk SCADA, smart meter data, and real-time analytics on Postgres!
📅 Nov 10–12
📍 USA Pavilion, Stand A.d117
Come meet the Tiger Data team!
👉 tigerdata.com/events/enlit-e…
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TimescaleDB (by Tiger Data) retweeted
Great technical deep dive into @TimescaleDB's new Adaptive Integer Compression (AIC).
Compression effectiveness depends heavily on the distribution and entropy of the data. There's no single encoding that works best everywhere, so AIC adaptively chooses among six different encodings for each block.
Combined with SIMD bitpacking and other low-level optimizations, the result is 5–7x faster decompression, 1.5x faster median queries, and ~10% smaller databases.
One particularly fun result: compressed batches small enough to fit inline rather than behind a Postgres TOAST pointer, eliminating thousands of unnecessary fetches.
tigerdata.com/blog/adaptive-…
Compression effectiveness depends heavily on the distribution and entropy of the data. There's no single encoding that works best everywhere, so AIC adaptively chooses among six different encodings for each block.
Combined with SIMD bitpacking and other low-level optimizations, the result is 5–7x faster decompression, 1.5x faster median queries, and ~10% smaller databases.
One particularly fun result: compressed batches small enough to fit inline rather than behind a Postgres TOAST pointer, eliminating thousands of unnecessary fetches.
tigerdata.com/blog/adaptive-…
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We're heading to #SPS2026! 🇩🇪
Join Tiger Data Nov 24–26 in Nuremberg to talk factory telemetry, machine sensor data, and real-time analytics on Postgres.
📍 Hall 6, Stand 133
👉 tsdb.co/sps-x
Join Tiger Data Nov 24–26 in Nuremberg to talk factory telemetry, machine sensor data, and real-time analytics on Postgres.
📍 Hall 6, Stand 133
👉 tsdb.co/sps-x
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TimescaleDB (by Tiger Data) retweeted
From e/acc to E/acc. What if one of AI’s biggest bottlenecks becomes one of its lasting contributions?
Some thoughts on energy abundance, the AI investment cycle, and the innovation we’re seeing across our customers.
linkedin.com/posts/mfreed_f…
Some thoughts on energy abundance, the AI investment cycle, and the innovation we’re seeing across our customers.
linkedin.com/posts/mfreed_f…
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On one query, 56% of AIC's speedup wasn't a faster decoder. It was a fetch that stopped happening.
AIC stores a small column's batch in 15 bytes, so it stays in the heap row and skips TOAST entirely. 52.5 ms to 11.9 ms.
tsdb.co/adaptive-compr…
#PostgreSQL
AIC stores a small column's batch in 15 bytes, so it stays in the heap row and skips TOAST entirely. 52.5 ms to 11.9 ms.
tsdb.co/adaptive-compr…
#PostgreSQL
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The October Tiger Chronicle is out 🐯
This month we’re spotlighting our EV customers:
⚡ Cactos cut storage from 15 TB to 1 TB and lowered monthly database spend by 55%
⚡ Easee tripled its charger fleet without architectural redesigns
Also in this edition: John Pruitt’s blog ive of our three memory systems wasn’t the most useful, where to find us at Enlit Europe and AWS re:Invent, upcoming workshops, and recent videos and podcasts.
Read the full edition 👇
lnkd.in/eg9bkmhX
This month we’re spotlighting our EV customers:
⚡ Cactos cut storage from 15 TB to 1 TB and lowered monthly database spend by 55%
⚡ Easee tripled its charger fleet without architectural redesigns
Also in this edition: John Pruitt’s blog ive of our three memory systems wasn’t the most useful, where to find us at Enlit Europe and AWS re:Invent, upcoming workshops, and recent videos and podcasts.
Read the full edition 👇
lnkd.in/eg9bkmhX
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We're heading to #EnlitEurope! 🇦🇹
Join Tiger Data Nov 10-12 in Vienna to talk grid telemetry, smart meter data, and real-time analytics on Postgres.
📍 USA Pavilion, Stand A.d117
👉 tigerdata.com/events/enlit-e…
Join Tiger Data Nov 10-12 in Vienna to talk grid telemetry, smart meter data, and real-time analytics on Postgres.
📍 USA Pavilion, Stand A.d117
👉 tigerdata.com/events/enlit-e…
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We built three agent memory systems. The most expressive wasn't the best.
Facts: too little structure.
Graph + SQL: dozens of turns to save a summary.
Tree + tools: usually one call.
Let agents manage memories, not the database.
tsdb.co/three-memory-x
Facts: too little structure.
Graph + SQL: dozens of turns to save a summary.
Tree + tools: usually one call.
Let agents manage memories, not the database.
tsdb.co/three-memory-x
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IIoT ingest, edge buffering, and the reconnect problem nobody tests for.
A remote site loses its uplink. The sensors keep reading for six hours. When the link comes back, do those rows land once, or twice? And what happens to every other site while that one catches up?
New guide on designing the reconnect path before you need it.
tsdb.co/cut-uplink-x
A remote site loses its uplink. The sensors keep reading for six hours. When the link comes back, do those rows land once, or twice? And what happens to every other site while that one catches up?
New guide on designing the reconnect path before you need it.
tsdb.co/cut-uplink-x
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In a benchmark against Ignition’s existing SQL Historian on Postgres, the new Enterprise Historian Module for TimescaleDB ingested 10x more data, queried it 10x faster, and used 10x less storage. Full SQL access throughout.
Travis Cox (@InductiveAuto) and Doug Pagnutti (@TigerDatabase) walk through how Ignition and TimescaleDB fit together, plus TimescaleDB for Ignition: unlimited tags, one price, optional HA cluster at no extra charge.
Thursday Oct 22, 9am PDT / 4pm GMT. Register and you'll get the recording either way.
tsdb.co/ignition-webin…
Travis Cox (@InductiveAuto) and Doug Pagnutti (@TigerDatabase) walk through how Ignition and TimescaleDB fit together, plus TimescaleDB for Ignition: unlimited tags, one price, optional HA cluster at no extra charge.
Thursday Oct 22, 9am PDT / 4pm GMT. Register and you'll get the recording either way.
tsdb.co/ignition-webin…
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TimescaleDB (by Tiger Data) retweeted
First, make Postgres really good at search. Then make search really easy to use.
That's the thinking behind pg_textsearch, pg_vectorsearch, and now searchgres.js.
That's the thinking behind pg_textsearch, pg_vectorsearch, and now searchgres.js.
Storing vectors is easy. Retrieving the right evidence is harder.
Introducing searchgres.js: BM25, semantic + hybrid search in Postgres, with hierarchy, metadata, regex + time filters.
Filtering belongs inside retrieval.
tsdb.co/searchgres-x
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Storing vectors is easy. Retrieving the right evidence is harder.
Introducing searchgres.js: BM25, semantic + hybrid search in Postgres, with hierarchy, metadata, regex + time filters.
Filtering belongs inside retrieval.
tsdb.co/searchgres-x
Introducing searchgres.js: BM25, semantic + hybrid search in Postgres, with hierarchy, metadata, regex + time filters.
Filtering belongs inside retrieval.
tsdb.co/searchgres-x
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TimescaleDB (by Tiger Data) retweeted
Really enjoyed joining Colby Clegg, CEO of @InductiveAuto, on the Ignited podcast.
I've long admired what they've built with Ignition: an open, flexible platform that has transformed how industrial teams build and deploy software.
We talked about our growing partnership, why @TimescaleDB is such a natural fit for industrial workloads, and how we're bringing the two together with the new Ignition Enterprise Historian.
Excited about what this unlocks for industrial engineers!
youtube.com/watch?v=di0ddO…
I've long admired what they've built with Ignition: an open, flexible platform that has transformed how industrial teams build and deploy software.
We talked about our growing partnership, why @TimescaleDB is such a natural fit for industrial workloads, and how we're bringing the two together with the new Ignition Enterprise Historian.
Excited about what this unlocks for industrial engineers!
youtube.com/watch?v=di0ddO…
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Your IIoT gateways drop for 10 minutes, then flush to Postgres at once.
At 80% of your ingest ceiling, that backlog clears in 40 minutes. At 95%, over 3 hours.
Few teams know where their 80% is. Here’s how to measure ingest headroom in SQL.
tsdb.co/bznvuggh
At 80% of your ingest ceiling, that backlog clears in 40 minutes. At 95%, over 3 hours.
Few teams know where their 80% is. Here’s how to measure ingest headroom in SQL.
tsdb.co/bznvuggh
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