Getting Started with InfluxDB 3 and Grafana Tutorial

This tutorial demonstrates how to build a local InfluxDB 3 and Grafana monitoring stack using a deterministic week-long smart-building telemetry dataset, with six zones sampled every five minutes and several built-in air-quality and energy-use incidents. It explains how InfluxDB stores and queries time-series data while Grafana provides dashboard visualizations, using Grafana’s built-in InfluxDB datasource with Flight SQL rather than requiring an external plugin or custom query language. Raw readings are written in line protocol to a building_telemetry table, then an InfluxDB Python Processing Engine plugin classifies each record into an iaq_assessment derived table containing risk scores, severity levels, recommendations, and conservative empty-room energy waste estimates. The guide emphasizes validating data ingestion and plugin output, preventing recursive triggers through table scoping, and securing Grafana with a read-only database token rather than an administrator credential. It then outlines an eight-panel Grafana dashboard with chained building and zone variables, time filters, aggregation, state timelines, statistics, gauges, and hotspot tables to identify unhealthy occupied rooms, pollution spikes, empty-room energy consumption, and priority investigation areas. Although based on building telemetry, the architecture is presented as reusable for industrial equipment, fleets, energy meters, networks, and application metrics, with suggested extensions including scheduled rollups, caches, retention policies, alerts, drill-down links, and dashboards as code.

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