Project background
Most IoT and industrial systems produce time-series data, yet custom analytics are rebuilt every time. The client wanted a reusable engine that handled storage, downsampling, and common analytical queries at scale.
Challenge
Balancing raw-resolution retention with query speed, exposing enough power for advanced users without overwhelming everyday ones, and maintaining consistent semantics across downsampled and raw data.
Approach & solution
We built the engine around a modern time-series store with continuous aggregates, plus a query layer that smooths over resolution differences. Operators express queries in terms that stay valid regardless of whether they hit raw or aggregated data.
Results & benefits
Analytics workloads that previously strained databases now run comfortably, and teams reuse the engine across multiple customer-facing products rather than rebuilding per project.
