Collecting and analyzing metrics allows to understand the behavior of systems and models. Metrics help us track performance, make informed decisions, detect anomalies before they escalate, and fine-tune our systems for maximum efficiency.
But raw metrics are hard to interpret on their own, especially as they evolve over time or span multiple dimensions. Effective visualization can highlight trends, outliers, and correlations in a way that simple logs and numbers cannot. And among the many tools available, Grafana stands out as one of the most popular open-source platforms for interactive data visualization.
Grafana at a Glance
Grafana is an open-source analytics and monitoring platform that enables users to query, visualize, and understand their data with ease. Originally developed for monitoring time-series data (like server performance metrics), it has grown into a tool used across various domains, from IoT to finance to machine learning.
There are some key features that makes Grafana most popular monitoring tool:
From bar charts to geomaps, Grafana offers a wide array of panel varieties to fit any data type.
Grafana’s strength comes from its ability to connect to a wide range of data sources out of the box and even more with community plugins.
Grafana support automated alerts based on threshold breaches, missing data, or complex query results.
Thanks for all this features and open-source nature, Grafana gives it's users benefits to:
Quickly spot anomalies in metrics, such as a sudden drop in accuracy or spike in latency.
Study how metrics evolve over time, enabling long-term system improvements and capacity planning.
Overlay and compare metrics from different sources to discover root causes.
Grafana has a continually expanding ecosystem of plugins that extends functionality through:
New panel types (e.g., heatmaps, 3D visualizations)
Data source integrations (e.g., AWS CloudWatch, Google Sheets)
Applications (e.g., Kubernetes dashboards)
Grafana is able to work with a wide range of data sources like Prometheus or Elasticsearch, which feed it with metrics, logs, and other data. Each data source comes with it's own query editor, which formulates custom queries according to the source’s semantics and structure. For example for Prometheus users, Grafana’s query editor supports PromQL (Prometheus query language) and visual interface for it.
For those who are new to Grafana, there is a hosted playground where you can explore dashboards and panels without installing anything.
The recommended way to setup Grafana is using docker:
docker run -d --name=grafana -p 3000:3000 grafana/grafanaYou can also check other installation options.
Interface
Core Grafana UI organization units are panels and dashboards. Panels are the fundamental building blocks of a Grafana dashboard. Each panel represents a visualization of data, pulled from one or more sources. Grafana provides a wide range of built-in panel types including pie chart, bar chart, gauge and much more others. Dashboards in Grafana bring together multiple panels into a single page. Dashboards can be saved, shared, and templated. They are the main interface for monitoring live data.
Dashboards support JSON export/import, making it easy to reproduce layouts across environments.
A pie chart displays data as segments of a circle proportional to the whole. Each segment corresponds to a value or measurement.
A bar chart is a visual representation that uses rectangular bars, where the length of each bar represents each value.
Gauges are single-value visualizations that allow you to quickly visualize where a value falls within a defined or calculated min and max range
Annotations and variables
Annotations in Grafana is a feature that let you mark events or changes directly on a dashboard for adding context to the data trends. This feature is particularly useful for correlating operational events (e.g., deployment, data update, outage) or experimental changes with shifts in data behavior.
Variables are placeholders for a values that make dashboards dynamic and reusable. Instead of hardcoding values, you can define dropdowns, filters, or text inputs to modify queries and panel behavior. Variables use cases include switching between environments (prod/staging), filtering by hostname or service, and selecting different ML models or runs.
Alerting
Grafana’s alerting system notifies you when something goes wrong. There is a wide range of notification channels like email, Telegram or Slack supported.
Alerts are defined on top of time series data with conditions based on thresholds or trend changes.
Each alert can be in one of three possible states:
OK: Everything is normal.
Pending: Condition met once but waiting for confirmation.
Alerting: Alert condition confirmed — notification is sent.
Instead of triggering as soon as the first condition is met, alerts remain in a pending state for a defined period of time. This helps filter out false-positive alerts and cases where the system recovers on its own.
You can also configure alert groups, silences, and notification policies to control how and when alerts are sent.
Conclusion
Grafana is a flexible and open-source visualization tool that helps to understand their systems through metrics. Its support for rich visualizations, intuitive querying, and alerting makes it a natural choice for monitoring both traditional infrastructure and machine learning workflows.