Industrial Data Intelligence
Machine telemetry arriving continuously from industrial equipment, turned into a consistent time-series store that engineers can query and chart.
- Sector
- Industrial / manufacturing
- Discipline
- Data Engineering
- Technology
- Python, ETL, InfluxDB, Grafana, Data Engineering
The problem
Telemetry from industrial equipment arrived continuously but was difficult to analyse consistently. Readings came in different units and intervals, gaps in transmission were indistinguishable from genuine zero readings, and every analysis began by rebuilding the same cleaning logic from scratch in a notebook.
What we built
We built Python ETL pipelines that ingest machine telemetry, normalise units and sampling intervals, distinguish missing transmission from real values, and write to a time-series database designed for range queries. Engineering dashboards read from that store, so every chart and analysis works from the same processed data.
Architecture
How the system fits together.
- 01
Ingest
Scheduled collection of raw telemetry from equipment endpoints and exports, with the untouched payload retained for replay.
- 02
Validate
Schema and range checks per signal. Out-of-range and absent readings are flagged rather than silently dropped.
- 03
Normalise
Unit conversion, timestamp alignment and resampling to a consistent interval across heterogeneous sources.
- 04
Store
Processed signals written to a time-series database with retention and downsampling policies for long-range queries.
- 05
Observe
Dashboards over processed signals, plus pipeline health monitoring covering freshness, volume and failure alerts.
Outcome
What the system delivers.
Capability delivered, stated plainly. We do not attach business metrics to this work that we are not in a position to verify.
- A single processed time-series store replacing per-analysis cleaning scripts
- Missing transmission distinguishable from genuine zero readings
- Reproducible pipeline runs with backfill and replay from retained raw data
- Signal health and pipeline freshness visible on a dashboard rather than discovered during analysis
- Python
- ETL
- InfluxDB
- Grafana
- Data Engineering