IoT devices generate continuous streams of sensor data, and most databases weren't built for it. The volume grows, queries degrade, and teams end up on an optimization treadmill that buys time but doesn't change the trajectory. If you're evaluating time-series databases, hitting the limits of your current setup, or just want to see what's possible when you extend PostgreSQL with the right primitives, this is a good use of an hour.
In this hands-on workshop, you'll build a real IoT analytics pipeline from scratch using TimescaleDB on Tiger Cloud. You'll write actual SQL, ingest real sensor data, and leave with a working Tiger Cloud instance you built yourself. Along the way, we'll cover the core primitives: hypertables for fast time-series storage, Hypercore columnar compression that cuts storage by 10x without losing a single data point, and continuous aggregates that keep analytical queries fast as data volumes grow.
What You'll Learn
- How to create a hypertable and model IoT sensor data alongside relational metadata
- How Hypercore columnar compression works and how to get 10x storage reduction while improving query performance
- How to write time-series queries using time_bucket() and hyperfunctions like last()
- How continuous aggregates give you self-updating materialized views for real-time dashboards
Not available May 28?
Try Tiger Cloud free or explore the
TimescaleDB IoT tutorial at your own pace.
