Definition
Grafana is an open-source monitoring and visualization platform that supports connecting to multiple data sources. Device Management Platform Telemetry data, performance metrics, and other data can connect to Grafana to build custom monitoring dashboards to meet operation and maintenance visualization needs.
Integration Methods with DMP
Grafana connects to Prometheus, InfluxDB, Time Series DB time-series databases, RESTful APIs, etc., through data source plugins. The Device Management Platform can write telemetry data to InfluxDB, Prometheus, etc., or provide aggregated query APIs for Grafana to pull. It supports visualization of indicators such as device online rate, alarm trends, OTA progress, and Device Health Score. Similar to Dashboard dashboards and NOC large screens, Grafana focuses on operations and maintenance monitoring scenarios and supports alert rules and multi-data source joint queries.
Typical Use Cases
In the IoT device management scenario, Grafana can be used to build device telemetry trend charts, regional health comparisons, alarm statistics, API latency monitoring, etc. It supports variables, templates, and permission management, facilitating team collaboration; it also supports embedding and sharing and can be integrated into NOC operations big screens. Compared with Tableau and Power BI, Grafana focuses more on operations and developers, suitable for time-series data and real-time monitoring.
Considerations for selection and implementation
Grafana is open-source and free, supporting self-hosting or Grafana Cloud. When integrating with DMP, ensure the data source is accessible and authentication is correct; for large-scale device scenarios, it is recommended to interface through an intermediate aggregation layer or data warehouse to ensure query performance. Supports alert notifications (email, DingTalk, Webhook) and can link with Device Management Platform alerts.
A panel query is not the device reporting path
In device management, Grafana queries and displays. It is not device access itself. A curve on a panel comes from a query against a data source: the time range, filter labels, aggregation, and refresh interval are all decided by the panel. A raw point a device has just reported has to be written into time-series storage, or prepared by a summary API, before it appears in the next query. "Now" on a panel is usually the latest complete time bucket, not the millisecond the message arrived. Variables that switch product, group, or region should bind to low-cardinality labels. Making a legend that expands without limit on device serial numbers lets the combination of labels drag the query down first. If an alert rule is configured on the panel side, it sees the query result, which is not necessarily the same record as the original event on the platform. Reports fit a daily rollup. A panel fits a recent trend while someone is on duty. Do not mix those time grains.
Metric shapes that belong on a panel
What is worth connecting is a metric already aggregated on a fixed interval, with a stable set of labels, that can be queried again and again: online count, how often a class of alarm occurs, how many devices sit in each stage of an upgrade, a percentile of API latency. Raw messages, and high-frequency waveforms from a single device, should stay in the device detail and be opened on demand, not on a shared panel that refreshes every ten seconds. The data-source account should only be able to read the agreed database or API. Do not draw charts with management credentials that can change devices. In a multi-tenant setup, the tenant boundary has to be in the labels or the query. "The viewer will filter" is not isolation. The refresh interval should be longer than the query itself takes, or requests pile up. When a figure such as mean time to repair is shown beside these, and that figure needs an explicit start and end rule, the panel should state the definition, so an unclosed ticket is not counted into time that has already been restored.
What follows when raw points go straight onto a panel
Treating every report from every device as its own series, and putting freely changing fields into the labels, inflates storage and queries. After the panel times out, someone reads that as the device having gone offline. A time bucket shorter than the reporting period leaves long gaps in the curve, which look like an intermittent fault. An average that does not also keep the extreme hides a short excursion, and the panel no longer matches the alarm list. Points with the same name from different products, drawn by one query, do not share a unit, and the axis stops meaning anything. Authentication with no data-scope limit means one panel link also brings out device counts from another organization. If the time zone and the dashboard default differ, the "peak last night" seen at shift change is shifted. A data source that is briefly unavailable, drawn as zero, makes the duty staff think every device dropped. "No data returned" and "the value is zero" have to be distinct.