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

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

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

@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

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…

Giuseppe from LogTide benchmarked TimescaleDB vs ClickHouse and MongoDB on 1M records. "We made the right call. But we're glad we have the numbers to prove it now." Full breakdown here: tsdb.co/logtide-benchmark-x

We’re live at Hannover Messe 2026! Come find the Tiger Data team in Hall 14, Booth L18 (April 20–24). Let’s talk Postgres, time-series, and industrial data. Book time with us: tsdb.co/n97tw0p2

Most databases live on a filesystem. Ours can be one. Mount it. List your tables like directories. Write a file, read it back with SQL. Giuseppe Pollio wrote up how TigerFS works here: tsdb.co/tigerfs-x #PostgreSQL #OpenSource

ClickHouse flushes on a 400ms timer. Right for bulk analytics. Wrong when microservices are logging in real time. TimescaleDB: 14,200 inserts/sec vs. 250. Queries: 0.47ms vs. MongoDB's 304ms. ow.ly/52h750YK9Os

Two things that make Tiger Cloud a better place to build with Postgres: 1. pg_textsearch now uses Block MAX-WAND for ranked search, with 40%+ smaller indexes. Competitive with the fastest Postgres search solutions. 2. Postgres 18 is the default. Async I/O, skip-scan, parallel GIN builds, uuidv7(). More: tsdb.co/hdeb5yc2 Start a free trial: console.cloud.timescale.com/…

MVCC is one of the best things about Postgres. It's also costing you more than you think if your rows never change after insert. Every row in Postgres carries a fixed 23-byte header, plus padding, that tracks who created it, who deleted it, and whether the transaction committed. That machinery exists so concurrent readers and writers never block each other. It's elegant engineering, and for mixed read-write workloads, it earns every byte. But sensor data doesn't get updated. Log entries don't get edited. Financial ticks don't change after they land. If you're running an append-only workload, those bytes are infrastructure for a problem you don't have. At high ingest rates, they add up: a 1KB sensor reading actually writes 2.5-3.5KB to disk once you account for headers, indexes, and WAL records. Autovacuum still runs continuously, even when there's nothing to clean up, because Postgres triggers it on insert volume alone. None of this is a bug. It's an architecture built for a different workload pattern, doing exactly what it was designed to do. Recognizing the mismatch is more useful than trying to tune around it. @mattstratton from Tiger Data (creators of @TimescaleDB) walks through the per-byte accounting, the write amplification chain, and what changes when the storage model actually fits the workload. tsdb.co/xbt73lov

Sensor data looks like rows, so most developers store it that way. Relational schema, transactional indexes, the patterns they already know. And it works, for a while. The problem is that sensor data doesn't behave like rows. It behaves like a time-ordered stream whose value declines with age. The questions you ask of it shift from point lookups to time-window aggregations. The volume grows with every device you add and every sampling rate you increase. And the things that happen in production, out-of-order timestamps, data replays, late corrections, break systems that were designed for clean, ordered inserts. By the time the architecture stops scaling, retrofitting it is expensive. The schema assumptions are load-bearing, and they're everywhere. This article from the Tiger Data (creators of @TimescaleDB) blog walks through what actually needs to be different: log-optimized ingestion, time-partitioned storage, lifecycle tiering that lets resolution and cost decline together as data ages. The kind of design that starts from how sensor data actually behaves, not how it looks at first glance. tsdb.co/2vem9kto

TimescaleDB 2.25 on Tiger Cloud: MIN/MAX/FIRST/LAST on compressed data now resolve from metadata instead of scanning chunks. Up to 289x faster. COUNT(*) with time filters can skip the time column entirely. Up to 50x faster. No query changes required. Details: tsdb.co/urhmd0ov Start a free trial: console.cloud.timescale.com/…

Most industrial machine monitoring is designed for large enterprises. High costs, rigid 10-device minimums, multi-year contracts. Small and mid-sized manufacturers, the backbone of the supply chain, are locked out. Takton built Sense Manufacturing to change that. Affordable machine monitoring that starts at one device. Approximately 30% lower total cost in year one, up to 70% lower in subsequent years compared to competitors. The team initially evaluated InfluxDB. It performed well for high-frequency data across a limited number of streams, but couldn't deliver on their production requirements: ingesting data from thousands of devices, each reporting power and vibration a few times per minute. They also wanted to avoid stack fragmentation. Their first product ran on Supabase using standard SQL and Postgres. Adding InfluxDB would mean maintaining a second query language and storage paradigm for a small team. Why they chose Tiger Data: ▪ Built on Postgres—kept a single SQL-based stack ▪ Designed for high-rate data ingestion from thousands of devices ▪ Tiger Cloud offloaded operational burden from a two-engineer team ▪ Hypertables provided automatic partitioning for reliable ingestion at scale Two months from first commit to devices live in customer facilities. One pilot customer's CNC machine failed after Sense flagged anomalous vibration readings for four days, but notifications weren't enabled. The failure cost $50,000 and three months of downtime. With alerts on, it would have been a $3,000 part change. How this startup shipped production IoT devices in 60 days, covered in this article by Farbod Moghaddam, CTO & Co-founder, Sense Manufacturing Inc: tsdb.co/yaaa0rao

Thanks @nvidia for mentioning Tiger Data (creators of @TimescaleDB), as part of your commercial open-source stack, during the NVIDIA GTC Keynote 2026 on structured data as the ground truth of AI.

The most important AI systems of the next decade will not live in chat windows. They will run factories, warehouses, energy systems, and fleets. AI is moving from analyzing operations to helping run them. Earlier this year, @nvidia introduced its Multi-Agent Intelligent Warehouse blueprint. What stood out was not just the agents themselves, but the architecture behind them. Instead of dashboards and alerts, specialized agents coordinate across machine telemetry, robotics systems, workforce operations, forecasting, and inventory to support real-time decisions in the physical world. You can see the architecture NVIDIA is proposing here: developer.nvidia.com/blog/mu… And the full blueprint here: build.nvidia.com/nvidia/mult… Systems like this depend on continuous access to operational data. Factories, warehouses, and energy systems already generate massive streams of telemetry from sensors, robots, PLCs, and machines. The challenge has never been collecting the data. The challenge is to reason quickly enough to act. The architecture starts to look like this: machines → telemetry → database → AI agents → decisions → machines This creates a real-time operational data loop. Agents do not operate in isolation. They need access to the operational history of the systems they manage. Telemetry, events, anomalies, and trends over time. In agent-driven industrial systems, the database becomes the memory layer for machines. Many industrial platforms already rely on Postgres and TimescaleDB to store and analyze time-series telemetry from machines and infrastructure. At Tiger Data, the company behind TimescaleDB, we see this pattern across industrial IoT platforms, fleet monitoring systems, and manufacturing analytics. The future of industrial AI is not just better models. It is systems that can continuously reason across operational data.

We're excited to share that Tiger Data is joining Chainguard Commercial Builds to provide secure, production-ready container images. Starting 3/17, TimescaleDB is available packaged inside the Chainguard Factory, a SLSA Level 3-compliant system that delivers: ✅ Minimal attack surface ✅ Zero CVEs ✅ Full provenance & SBOMs ✅ FIPS readiness ✅ SLAs for vulnerability remediation Enterprises shouldn't have to choose between getting value from their software and maintaining the security layers beneath it. This partnership means your teams get TimescaleDB with the most secure software supply chain on the market - without the overhead of building, patching, or monitoring images yourself. 👉 tsdb.co/wdk5fxrn #SoftwareSupplyChain #Chainguard #TimescaleDB

We shipped a lot in Q1. TimescaleDB 2.25 with up to 289x faster queries on compressed data. Postgres 18 by default. Full-text search that rivals the fastest Postgres search engines. Azure Marketplace self-serve. New AWS region. Better Console UX. Full roundup: tsdb.co/hdeb5yc2 Start a free trial: console.cloud.timescale.com/…