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Power Insight

Embedded data platform for generators collecting device telemetry, running ETL, and surfacing operational reports in Grafana.

Year · 2023Stack · Flask · Bash · Dotnet · Angular

Last updated · July 2023

Embedded data platform for generators collecting device telemetry, running ETL, and surfacing operational reports in Grafana.

Technologies
11
Year
2023
Live
N/A
Source
N/A

What It Is

Power Insight is an embedded-system data platform built for generator fleets and industrial operators who need dependable telemetry from the field. It collects readings from devices, stores them securely, runs ETL to keep data accurate, and exposes real-time dashboards and reports so teams can monitor health, spot anomalies, and plan maintenance before failures occur.

Instead of manual log pulls and ad hoc spreadsheets, operators get a connected pipeline from device to database to visualization designed for environments where connectivity, encryption, and uptime matter.

The Problem We Solved

Generator operations teams often face the same friction:

  • Device data is scattered across binary logs, local files, and one-off scripts
  • There is no single place to see fleet-wide trends or per-unit history
  • User and role management for dashboards is bolted on late in the project
  • Deployments to embedded hardware are slow, error-prone, and poorly documented

Power Insight unifies collection, storage, transformation, and reporting so engineering and operations share one source of truth from the edge device through InfluxDB to Grafana charts.

What We Work On

Edge data collection

Capture generator telemetry with Python and Bash scripts tuned for embedded constraints, including custom binary formats and encryption where required.

ETL & analytics pipeline

Transform raw device output into queryable time-series data in InfluxDB, feeding algorithms and Grafana panels used for operational reporting.

Access & administration

Build user management flows using the Grafana DB API with Angular and ASP.NET UIs so the right people see the right dashboards.

Network & deployment automation

Automate provisioning and updates on embedded systems with Bash network scripts and Azure/AWS pipelines for OS and application rollout.

How It Works (In Simple Terms)

  1. Collect: Scripts on or near the generator gather runtime metrics and events.
  2. Store: Data lands in InfluxDB (and related stores) with structure suited for time-series queries.
  3. Transform: ETL jobs normalize, enrich, and validate records before they power analytics.
  4. Visualize: Grafana dashboards show live and historical performance for operators and engineers.
  5. Operate: Automated deploy pipelines keep edge software and services up to date across the fleet.

The system is built for continuous monitoring, not one-off exports so maintenance and capacity decisions are based on current fleet behavior.

Key Outcomes

  • Reliable ingestion: Repeatable capture from embedded devices into a central time-series store.
  • Actionable dashboards: Grafana views tailored to generator KPIs and fleet comparisons.
  • Controlled access: Role-aware user management tied to reporting tools.
  • Faster rollouts: CI/CD-style pipelines reduce manual deployment on constrained hardware.
  • Better data quality: ETL steps catch inconsistencies before they skew operational decisions.

Technologies & Approaches We Used

Area What we used Why it matters
Collection Python, Bash Scripts suited to embedded Linux and device-specific formats
Backend / services Flask, .NET APIs and services for data and user-management features
Frontend Angular Admin and configuration UIs for operators
Time-series DB InfluxDB Efficient storage and queries for high-volume telemetry
Visualization Grafana Industry-standard dashboards for ops and engineering
Automation Azure, AWS pipelines, Docker Repeatable builds and deployments to edge and cloud
Testing Katalon, Selenium Regression coverage for critical UI and integration paths

Approach in practice: We separated edge collection from central analytics devices stay thin and resilient while ETL and Grafana handle aggregation. DevOps pipelines were treated as part of the product so firmware, OS packages, and application releases could be promoted with the same rigor as backend code.

Who It's For

  • Generator manufacturers and fleet operators
  • Embedded engineering teams shipping connected industrial products
  • Reliability and maintenance groups monitoring uptime and fuel/load metrics
  • Organizations that need audited, time-series history across many deployed units
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