Escaping IIoT Pilot Purgatory: Why Your Database Breaks at Scale
A presentation at IoT Tech Expo North America 2026 in in San Jose, CA, USA by Matty Stratton
ESCAPING IIoT PILOT PURGATORY of IIoT pilots never reach production. The database is usually where they die. Why your database breaks at scale / Matty Stratton, Tiger Data
01 / WHY THE PILOT WORKED The pilot lies to you. PoC PRODUCTION millions of rows billions of rows Inserts: milliseconds Inserts: backlogs forming Dashboards: instant Dashboards: loading… Cost: rounding error Cost: someone notices Different physics. Same database.
02 / THE NATURE OF IIoT DATA IIoT data is multiplicative. volume = n × v × r × t tags data/tag frequency time ONE SMALL FACTORY / 10,000 tags × 1 Hz × 1 year 315 billion rows • 37.5 TB on disk
03 / THE PERFORMANCE ENVELOPE Every IIoT system has an envelope. Three walls define the box: DEPTH 01 retention STORAGE cost compounds with retention ↑ 02 INGEST throughput collapses without warning in spec 03 QUERY aggregates slow with data growth WIDTH → tags / sensors
WALL 01 / STORAGE Storage compounds quadratically. cumulative cost ∝ n × r × T² Trivial in month 1. Exorbitant in year 5. Y1 Source: Pagnutti, tigerdata.com/blog/the-iiot-postgresql-performance-envelope Y2 cumulative storage cost Y3 Y5
WALL 02 / INGEST Ingest fails without warning. WAL writes and index updates grow with DB size. THE RULE System looks healthy for months. Then hits equilibrium with no runway. Backlog forms. Backlog grows. You don’t catch up. <80% of max ingest. One bad VACUUM, one network blip, one burst, and you’re behind forever.
WALL 03 / QUERY Queries degrade linearly. Indexed deep queries: O(log t·r) PLUS THE SLOW ROT Aggregate dashboards: O(t·r) linear with retention. • Planner stats go stale Every dashboard you’ve ever shipped uses GROUP BY and AVG. • VACUUM falls behind They scan every row in the window. The window keeps growing. • Server hardware drifts • Sequential scans creep back in DASHBOARD LOAD TIME milliseconds → seconds → minutes → loading…
04 / THE WRONG FIXES Hardware buys time. It doesn’t move the ceiling. 01 VERTICAL SCALING 02 HORIZONTAL / SHARDING 03 ADD A SECOND DB Bigger box. More boxes. InfluxDB. A historian. More RAM, CPU, disk. Split tables, shard the DB. Leave Postgres for analytics. Diminishing returns. Replication lag. Pipelines. Sync. Drift. Hard ceilings. Cross-node coordination. Two systems, one team. Doubling RAM never doubles Often breaks the ACID A permanent operational tax. anything that matters. guarantees you chose PG for. New query language to learn. All three push the wall out. None of them change the math.
05 / WHAT ACTUALLY MOVES THE CEILING Change the math. Stay on Postgres. WALL 02 → INGEST WALL 03 → QUERY WALL 01 → STORAGE Hypertables Continuous aggregates Hypercore (columnar) Auto-partition by time. Indexes stay in Dashboards read precomputed rollups, not Older chunks compress columnar. Same SQL, memory per chunk. raw rows. less disk. PG cliff at ~25M rows. Hypertables stay flat. 250-1500 ms → 0.4 ms Benchmarks: tigerdata.com/blog/how-timescaledb-expands-postgresql-iiot-performance-envelope All three primitives ship as a Postgres extension. Same SQL. Same tooling. No second database. 80-95% compression. $150K/yr → ~$15K.
06 / THE OUTCOME AXPO / ENERGY UTILITY “We connected 2 power plants. After half a year the database was completely full, we couldn’t insert or query any data, and the whole system crashed.” Emanuel Joos, Lead Software Engineer, Axpo Now: 20+ systems. Still running. Map your envelope. tigerdata.com/blog/the-iiot-postgresql-performance-envelope
WHO’S TALKING Matty Stratton Sign up for a trial and get $1000 in credits! https://tsdb.co/matty-iot-tech Head of Developer Advocacy & Docs at Tiger Data and founder of the Arrested DevOps podcast. I live in Chicago with too many pets. Passionate advocate for helping developers make better database decisions and get more out of Postgres and time-series data linkedin.com/in/mattstratton/ (slides at speaking.mattstratton.com)
74% of manufacturers get stuck in pilot purgatory, and the database is usually where it falls apart first. This session breaks down the performance envelope that every IIoT system hits when scaling from PoC to production: storage costs that compound quadratically, ingest bottlenecks that appear without warning, and query degradation that turns real-time dashboards into loading screens. You’ll learn how to map your system’s limits before you hit them and what architectural choices actually move the ceiling instead of just buying time.
Resources
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