🐯 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 database, purpose-built 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

We're at ISA Automation Summit & Expo, Sept 27-29 in Orlando. Booth 414. Come talk industrial telemetry, SCADA data, and why you don't need a second database for real-time analytics. tsdb.co/isa2026-x

What does it look like when the historian experience and the database underneath it are designed to work together? With Ignition Enterprise Historian, you configure the historian in Ignition. Connect it to the TimescaleDB environment. Then let the integration handle historian-specific database setup and configuration. No separate database design and tuning workflow. Come see it at Booth #2 at ICC 2026.

One of those weeks where you can feel a market shifting in real time. At the Ignition Community Conference, I kept running into existing open-source @TimescaleDB users across data centers, energy, O&G, manufacturing, and other industrial systems, alongside a lot of excitement around the new first-party Ignition Enterprise Historian, powered by TimescaleDB Enterprise. Huge thanks to Colby Clegg, @InductiveAuto's CEO, for inviting me to join him onstage during the keynote to announce our new product offering and strategic partnership in front of 2,000+ attendees. Feels like a real moment for the modern industrial data stack. #ICCUnleashed

pg_textsearch is now supported natively in @googlecloud's AlloyDB and Cloud SQL for PostgreSQL. Industry-standard BM25 ranking inside two of the most widely used managed Postgres services. No separate search infrastructure to run. Public preview today, GA later this year → tsdb.co/pg-textsearch-gcp

Industrial historian workloads have changed. More tags. Higher-frequency data. Longer retention. At #ICC2026, Tiger Data CTO Mike Freedman went under the hood of how Ignition Enterprise Historian + TimescaleDB are built to meet those demands at scale. Modern historian. Industrial scale.

Big news from #ICC2026: Tiger Data is now a strategic partner of @InductiveAuto. 🐯🤝 Today, CEO Colby Clegg and Tiger Data CTO Mike Freedman took the stage to introduce Ignition Enterprise Historian, powered by TimescaleDB. We’re excited to build what’s next for industrial data together.

Ignition Community Conference kicks off tomorrow! Find the Tiger team at Booth #2, Sept 22–24. Come talk historians, industrial scale, TimescaleDB, and what’s next for Ignition. Bring us your hardest historian questions. tsdb.co/icc26-team-x

Vector search in Postgres is simple until your data grows. Then it's an index design problem. Four constraints decide the right index: memory, recall, write volume, filter selectivity. HNSW, IVFFlat, StreamingDiskANN, or hybrid with BM25. Decision guide: tsdb.co/v9n4ire4

160x faster writes on compressed data. Real-time analytics on live operational data used to force a choice: split to another database or accept latency. Not anymore. Handle constant data streams in Postgres without re-architecting. Postgres for the physical AI world. See how you can achieve 160x faster writes → tsdb.co/97di8nmj

Doug Pagnutti from Tiger Data on his time as an automation engineer: the operators at the plant used to race the SCADA dashboard to see who was faster. The operators won. After 18 months the database couldn't keep up with the time-series workload. Tiger Data deploys TimescaleDB on PostgreSQL so manufacturers keep every tag at full resolution without picking and choosing what to store. Partner content with @TigerDatabase. They're at IMTS this week. #tigerdata_ai #IMTS2026

Doug Pagnutti from Tiger Data on manufacturing data 10 years ago vs. now: back then, plenty of data, no way to use it. Machine learning use cases existed but couldn't justify the storage cost. Now GenAI can use the data, but most plants didn't keep enough of it at the right resolution. Partner content with @TigerDatabase. #tigerdata_ai #IMTS2026

Doug Pagnutti from Tiger Data lived the classic factory data problem firsthand: buying bigger servers that didn't help, dropping tags to make room, disconnecting hard drives full of production data. 10 years later, GenAI finally makes that data valuable, but most plants already deleted the high-resolution records those models need. TimescaleDB solves that at the architecture level. Partner content with @TigerDatabase. Find out more from Tiger Data at #IMTS2026. #tigerdata_ai

Doug Pagnutti from Tiger Data: machine learning found humidity was driving the tuning on a powder dosing system, something the team never suspected. On the troubleshooting side, AI helps engineers reach root causes faster instead of applying band-aids. Partner content with @TigerDatabase. Tiger Data at IMTS, Sept 14-19. #tigerdata_ai #IMTS2026

Doug Pagnutti from Tiger Data on a use case he wouldn't have expected a couple of years ago: energy management. Manufacturers are slowing down energy-intensive processes when electricity prices spike and adjusting production around the grid. With power getting more complicated, real-time data for those decisions saves real money. Partner content with @TigerDatabase. Live from IMTS, Sept 14-19 in Chicago. #tigerdata_ai #IMTS2026

Doug Pagnutti from Tiger Data built a SCADA system at a manufacturing plant. It worked great for about 18 months. Then the database filled up, dashboards slowed to a crawl, and he ended up shelving hard drives full of production data nobody could access. TimescaleDB on PostgreSQL handles both relational and time-series data with 90% compression, so the database keeps up. Partner content with @TigerDatabase. Visit Tiger Data at #IMTS this week. #tigerdata_ai #IMTS2026

Listen to my conversation with @acoustik of @TigerDatabase on Spotify: bit.ly/3v1R0Tu Apple: bit.ly/4bTCwpD Youtube: bit.ly/3uXthnv LinkedIn: bit.ly/3Xs8GQP Website: svppro.com ~~~~~~~~~~~~~ This episode is brought to you by Nebius — the ultimate cloud for AI innovators. Nebius provides AI infrastructure you can count on, combining reliability and speed with flexibility and engineering support unmatched by hyperscalers. AI leaders like Meta, Shopify, and Higgsfield already partner with Nebius to run their AI workloads. Plus, venture-backed startups can save up to $150,000 on compute costs when they apply for access. Visit nebius.com or nebius.com/startups to learn more ~~~~~~~~~~~~~ #ArtificialIntelligence #PostgreSQL #AIInfrastructure #Database #AIEngineering

🎉 Week 130 — Venture with Grace Week 130 explored AI-native growth, healthcare voice agents, intellectual property, autonomous construction, AI voice infrastructure, and the next generation of enterprise data systems. Week 130 lineup: @georgebonaci, VP at @tryramp: AI-first growth and demand generation in the modern B2B landscape @jain_ankit, CEO at @InfinitusAI: AI voice agents in healthcare and automating complex patient and provider workflows Paul Lee, CEO at Patlytics: AI, patents, and transforming intellectual property strategy with intelligent technology @bsofman, CEO at @BedrockRobotics: Autonomous construction and bringing AI-powered robotics into the physical world @rissa_cao, CEO at @FishAudio: AI voice infrastructure and building expressive, scalable voice technology @bryantchou, CEO at @ployai: AI agents, marketing automation, and the future of web growth @acoustik, CEO at @TigerDatabase: Postgres, AI agents, and the data infrastructure powering intelligent applications Another packed stretch of conversations — with a clear takeaway: AI is moving deeper into real-world workflows, from healthcare and construction to marketing, voice, IP, and the data infrastructure underneath it all. More coming next week. See you live 👋 #VentureWithGrace #AI #startups #founders #vc #AIAgents #AIInfrastructure #Robotics #EnterpriseAI