Grafana is an open-source platform for turning operational data into interactive dashboards. For a digital twin, it is a practical choice when continuous sensor readings, status updates, and derived metrics need time-series views without a custom charting application. section 6.4 section 6.4.1
A dashboard can bring live and historical data from different sources into one view through plugins. Line charts, gauges, and other panels help operators inspect trends, narrow the time window, compare metrics, and spot anomalies. Built-in alerting, layouts, and permissions also reduce the infrastructure a custom visualization layer would need. section 6.4.1 section 6.4.2
docker run \ --name grafana \ -p 3000:3000 \ -v grafana-storage:/var/lib/grafana \ -e GF_INSTALL_PLUGINS="yesoreyeram-infinity-datasource" \ --add-host=host.docker.internal:host-gateway \ grafana/grafana-oss:13.0.1
This Docker command starts Grafana OSS 13.0.1, persists its data in a named volume, and installs the Infinity data-source plugin for the example integration. section 6.4.2
From measurements to a time-series panel
An Infinity data source can call a GraphQL API for a space’s measurements over Grafana’s selected time range. The response contains nested measurements and timestamped values; a jq transformation flattens them into time, value, metric, and label fields for charting. The resulting dashboard can show voltage fluctuation over time in a single panel. section 6.4.2
.data.spaces[]
| .name as $space
| .measurements[]
| .name as $metric
| .values[]
| {
time: (.timestamp | tonumber),
value: .value,
metric: $metric,
label: $space
}
The jq expression shows the crucial data-shaping step: iterate through spaces, measurements, and values, then emit one chart-ready record per timestamped value. section 6.4.2
Choose Grafana chiefly for operational monitoring with sensor streams, fast time-range queries, and alerting. Power BI and Tableau fit reporting or historical exploration better; Plotly Dash offers more control for bespoke interactive applications. A dashboard tool does not eliminate the need to configure queries and transformations—or to assess whether a specialized view calls for custom development. section 6.4.2
Table 6.3 Comparing visualization platforms for digital twin applications
|
Consideration
|
Grafana
|
Power BI
|
Tableau
|
Plotly Dash
|
|---|---|---|---|---|
|
Primary strength
|
Real-time operational monitoring
|
Business reporting and KPIs
|
Exploratory analytics
|
Custom interactive applications
|
|
Real-time and streaming
|
Native support with built-in alerting
|
Limited streaming capabilities
|
Primarily static/historical analysis
|
Supported through custom development
|
|
Ease of use
|
Moderate; requires DevOps and query skills
|
Low-code and analyst-friendly
|
Analyst-friendly visual tooling
|
Requires Python development
|
|
Customization
|
Plugin extensibility
|
Moderate
|
Moderate
|
Extensive application-level control
|
|
Typical digital twin role
|
Operational dashboards
|
Executive reporting
|
Historical trend analysis
|
Simulations and bespoke UIs
|
|
Cost model
|
Open source or managed cloud
|
Per-user licensing
|
Per-user licensing
|
Open source plus hosting costs
|
The comparison separates real-time monitoring from reporting, exploratory analysis, and custom applications, including their different skill and cost considerations. section 6.4.2
