Exploring data in Dynatrace: From metrics to dashboard tiles

zietho — Fri, 06 Mar 2026 12:29:15 GMT

This guide walks you through exploring and visualizing metrics in Dynatrace.

From browsing metrics to narrowing your scope with filters, comparing entities with Split by, and keeping charts readable with limits and sorting. The Explore interface builds a DQL query behind the scenes, so when you're ready for deeper analysis, you can open and jump to a DQL tile to advance your analysis.

Below, each step has its own section so you can jump straight to what you need and try the complementary examples on the Dynatrace playground to experience and play with the results yourself.

Start with Explore Metrics

In a dashboard or notebook, open Add and choose Explore Metrics (or any other data type, such as Logs, Events, or Traces) to explore existing data for the respective type in the UI.
Selecting Metrics creates a new empty tile and opens the detail view to browse and select a metric.

Browse for metrics

Once the new tile has been added, click on Select a metric to open the metric browser.
Use the tree on the left to switch between categories, to narrow down available metrics. Hover over the metric’s name in the middle section and compare it based on the additional information (key, unit, type, description, fields) provided to the right. Once you have found the right metric, click on its name.

Check out the finished "1 - Plot a Service response time metric" example on the Dynatrace playground.

Narrow the scope

Narrow the scope with additional commands like filters. After selecting Filters, click into the newly added filter field below the selected metric, then add a field (e.g., k8s.cluster.name) and enter a value.
The filter field supports different operators like =, !=, <, >, in (…), and wildcards with *. Only relevant operators will be displayed, depending on the type of selected key/field. Wildcards, depending on where you use the wildcard, will be resolved to a start with (at the beginning), ends with (at the end), or contains (beginning and end) filter.

❗Note: We currently don’t allow wildcards inside a filter term.

Find the finished "3 - Narrow the scope by adding a filter" example on the Dynatrace playground.

Break it down

To compare the same metric across entities (for example, one line per cluster or pod), add Split by and select the field you want to compare by. It's important to know that most metrics already come with both the id and name of an entity auto-enriched. For example, a k8s cluster metric has the k8s.cluster.name and the k8s.cluster.uid already enriched on the metric.

Use auto-enriched name fields as the primary split key when names are unique. This improves readability and ensures stable queries; if an auto-enriched name is missing, fall back to the Dynatrace internal ID. For example, dt.smartscape.k8s_cluster, which lets the Explore interface attempt an automatic name join. Prefer human‑friendly names when available and deterministic IDs when not.

Find the finished "2 - Break it down with split by" example on the Dynatrace playground.

Keep results limited and sorted

If your split produces too many series, add a Limit and a Sort command to focus on the top-N results. This is especially important for visualizations such as line, area, bar, categorical bar, or heatmaps, where more than N items can’t be meaningfully visualized at once.
In most cases, you don’t want to see all available entities, which in the observability space can easily go into tens of thousands, or even more. Typically, it’s about identifying the “heavy hitters” and analyzing them further.

Find the finished "4 - Focus on the question by using sort and limit" example on the Dynatrace playground.

Going beyond Explore

Explore interface builds a DQL query behind the scenes. Whenever you want to start a deeper analysis, for example, one that involves multiple data types and advanced DQL features you can:

Open DQL to Show DQL, copy it, and use it in another tile, or
Use Create DQL section/tile to continue with more advanced commands in a new tile.

Find the finished "5 - Advance with DQL (converted tile based on 4)" and "6 - Advance with DQL by adding new calculated fields" examples on the Dynatrace playground.

Data Explorer (previous Dynatrace): where to start

If you’re using Data Explorer (from the previous Dynatrace) to explore and visualize metrics, use the official docs as the primary reference:

Data Explorer overview (concepts + query components like Split by / Filter by / Rate):
https://docs.dynatrace.com/docs/shortlink/explorerdocs.dynatrace
Data Explorer quick start (templates + build-from-scratch walkthrough):
https://docs.dynatrace.com/docs/shortlink/explorer-quick-startdocs.dynatrace
💡 Tip: If your end goal is a reusable tile, check the official guidance for moving visualizations forward using Open with into Dashboards or Notebooks:
https://docs.dynatrace.com/docs/shortlink/dashboards-upgrade-classic-to-latestdocs.dynatrace

topic Exploring data in Dynatrace: From metrics to dashboard tiles in Dashboarding