Tiger Data Published a Case Study on ControlCom Connect
Tiger Data, the company behind TimescaleDB, published a case study on how ControlCom Connect handles industrial time-series data: How ControlCom Turns 300+ Million Monthly Facility Data Points Into Instant Answers .
The article covers the numbers we work with every day: 1,000 to 10,000 data points per second coming off thousands of assets, more than 300 million data points a month, and dashboards that re-bucket years of history without making the operator wait. It also tells two stories our customers already know, the $160,000 in utility billing errors a healthcare facility found in its first month on the platform, and the backup generators a hospital discovered had been left in manual mode after service.
Why the database choice matters to operators
ControlCom Connect was PostgreSQL-based from the start, and we wanted to keep it that way when the data volume grew. TimescaleDB let us stay on the database we trusted instead of bolting on a separate time-series system with its own query language, its own tooling, and its own failure modes.
The same database runs in two places:
- On every Edge Server, a local TimescaleDB instance keeps collecting and storing data when site connectivity drops, then replays it to the cloud when the link returns.
- In the cloud, a central instance on Tiger Cloud acts as the system of record across every site in a portfolio, feeding dashboards, alarms, and reports.
That is the hybrid architecture that carried our customers through a 15-hour AWS outage with a complete data record, and it is the same design whether you run one lift station or a hundred facilities.
No lock-in is a feature, not a slogan
Because the whole stack is Postgres, your data is reachable with plain SQL and exportable to CSV or SQL at any time. If you ever leave, you take everything with you. That matters to the facility managers and integrators we work with, who have all inherited at least one system where the data was held hostage by the vendor’s format.
Thanks to the Tiger Data team for taking the time to dig into the architecture. The full case study is on their blog: read it here .