Tiger Data (creators of TimescaleDB)

One thing is clear from the breadth of new customers signing up for TimescaleDB every day: The pace of innovation across the energy ecosystem is remarkable. Solar. BESS. LNG. Grid infrastructure. AI data centers. It's not just software. It's advanced materials, battery chemistry, power electronics, grid dynamics, control systems, and AI. Innovation is happening across the entire stack. It really does feel like the beginning of a new industrial revolution.

Every ship in the world broadcasts its position every few seconds. At VesselAPI, those reports arrive faster than any single-row insert loop can keep up with. They ran it on MongoDB for a year. Then they stopped. The data looked like documents. You could serialize a position report as JSON. MongoDB stored it fine. But vessel positions are measurements. Timestamp and location aren't just metadata. They answer questions like: Where was this ship two hours ago? What's within 50km of Rotterdam right now? Show me everything through the English Channel since Tuesday? At that volume, fighting the grain of your database gets expensive fast. The TimescaleDB migration gave them hypertables with 1-hour chunks, automatic compression segmented by vessel identifier, and retention policies that just drop aged-out partitions in milliseconds instead of a cron job that once failed silently for a week. But the really clever engineering is the spatial query layer: a three-stage filter using H3 hexagonal indexing, PostGIS, and chunk pruning that narrows the candidate set from millions of rows down to hundreds. And then there's the bug. A single mismatched struct tag, a leftover bson serialization name from the MongoDB era colliding with the new JSON field name, broke the nightly ports rebuild. The table came back empty, and every port event created in that window got a null join key. The fix was changing one string. The deeper fix was finding nearly 240 more vestigial bson tags scattered across the codebase, each one a potential repeat. All of it runs comfortably on a single Postgres box, with TimescaleDB doing the heavy lifting. Worth the read whether you're considering a migration or not 👇 https://lnkd.in/g2MTmNib #PostgreSQL #TimescaleDB #TigerData

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 → https://tsdb.co/bjsayt4i

Okay... Maybe a new highlight for my career at Tiger Data ❤️ 🐯 I just finished being interviewed on the Robot Report podcast https://lnkd.in/gCAuVzMR I've been listening to their pod since someone suggested it to me at Hannover Messe so it was really cool to be a part of it. And I don't think I messed it up -well, it releases on Friday, so maybe I did screw up and just don't know yet.

Happy to share a milestone in my open-source journey! My latest guide, 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲𝗗𝗕 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗖𝗹𝗼𝘂𝗱𝗡𝗮𝘁𝗶𝘃𝗲𝗣𝗚, has been published on the official Tiger Data documentation website. Over the past few years, I've worked extensively with PostgreSQL, Kubernetes, and CloudNativePG in production environments. This guide brings together practical lessons and production-focused recommendations that I hope will help engineers deploy and operate TimescaleDB more confidently on Kubernetes. This is the first article in a series focused on real-world operations. I'm already working on the next guide 𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝘂𝗽𝗴𝗿𝗮𝗱𝗶𝗻𝗴 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲𝗗𝗕 𝘄𝗶𝘁𝗵 𝗖𝗹𝗼𝘂𝗱𝗡𝗮𝘁𝗶𝘃𝗲𝗣𝗚, followed by topics like backup & recovery, monitoring, and production best practices. Many thanks to the 𝗧𝗶𝗴𝗲𝗿 𝗗𝗮𝘁𝗮 (𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲𝗗𝗕) team and the open-source community for building and supporting such an incredible ecosystem. 📖 𝗔𝗿𝘁𝗶𝗰𝗹𝗲 𝗹𝗶𝗻𝗸 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁. I'd love to hear your feedback and learn about your experiences running PostgreSQL or TimescaleDB on Kubernetes. #TimescaleDB #Tiger Data #PostgreSQL #CloudNativePG #Kubernetes #OpenSource #PlatformEngineering #CloudNative #DevOps #Database

AI infrastructure is in the midst of a generational buildout, and TimescaleDB is appearing everywhere across the AI data center and energy OT stack. Now we’re building the systems-integrator ecosystem around it. We’re hiring a US-based Channel Sales Manager to help scale our industrial GTM motion. You’ll work closely with me and our head of industrial partnerships to grow this major channel. Come build it with us. Link below. #BuildersWanted

Your query needs two columns. PostgreSQL reads all fifteen. That's not a bug or a tuning problem. It's how row-oriented storage works: data is stored as contiguous heap tuples on disk, and Postgres reads the entire tuple to access any column within it. The number that makes this concrete is the read amplification ratio: total row width divided by the width of the columns your query actually needs. A sensor_readings table with 15 columns at 200 bytes per row, queried for ts and temperature (12 bytes total), gives you a ratio of 16.7x. For every byte you use, Postgres reads 16.7 bytes from disk. At 100 million rows, that's 18.6 GB read instead of 1.14 GB. At 500 MB/sec sequential read, 38 seconds instead of 2. No index changes this. B-tree indexes solve row selection. The bottleneck here is row width. Those are different problems. Columnar storage fixes the I/O by storing each column's values contiguously. But pure columnar adds write overhead under high-frequency ingestion. Hypercore keeps recent data in row format for fast writes, and automatically converts older data to columnar based on a compression policy. One table, one SQL interface, the right storage layout for each age tier. Post has the diagnostic queries to measure your own read amplification ratio before you change anything. https://lnkd.in/gUZXKvbi #PostgreSQL #Hypercore #Analytics #TimescaleDB

I just wired up Node-RED to TimescaleDB in less than 10 mins!   Here's the setup: a sensor publishes temperature over MQTT every few seconds, and Node-RED picks it up and writes it straight into a TimescaleDB hypertable running on Tiger Cloud.   ✅ MQTT simulator built with a simple function node ✅ PostgreSQL node configured for TimescaleDB on Tiger Cloud ✅ Verified live inserts using pgAdmin   If you are building any kind of sensor monitoring pipeline, this is one of the fastest ways to get from "MQTT topic" to "queryable time series data."   Full walkthrough is on IoT Frontier, link below. 👇   #IoT #NodeRED #TimescaleDB #MQTT #IIoT #TigerData #PostgreSQL Timescale Tiger Data (creators of TimescaleDB)