Databricks AI/BI Dashboards: Build and Check a View

You can use Genie Code↗ (opens in a new tab) to help build Databricks AI/BI dashboards, then review the queries and metric definitions before relying on a view. Start with the question the dashboard must answer, rather than asking for an attractive collection of charts. fenbs can keep the definitions, review results, and unfinished checks so the next person or assistant knows what the numbers mean.

How do you start a dashboard request?

Databricks documents Genie Code as capable of building dashboards and working inside the dashboard experience. Its navigation guide↗ (opens in a new tab) gives these entry points for starting a request. This is an assistant-led starting workflow; the cited pages do not provide a complete manual procedure for creating and publishing an AI/BI dashboard.

1. Open the Genie Code pane from the upper-right corner of the workspace page.
2. Describe the business question, the intended audience, and the approved data you want the dashboard to use.
3. Ask for a proposed metric definition and query explanation before any execution: “Explain; don’t run code.”
4. Review the proposed calculation, then give a narrow instruction for the next change you want to make.
5. For a full-page start instead, select Code in the workspace-home prompt box and submit the request, or choose Maximize in the pane.

Confirm the creation and publishing procedure with your workspace owner before sharing a finished view. Do not substitute an older tutorial’s menu names for current controls. The useful starting result is a small view you can verify, with a clear account of how its figures were calculated.

What should the first view answer?

Choose one decision the reader needs to make. For example, a hypothetical support team may want to know whether its unresolved workload is growing. That question needs a definition of unresolved, the point in time being measured, and a rule for reopened work. A chart cannot settle those choices for the team.

Help me plan a view of unresolved support requests by week.
First list the definitions we must agree on.
Explain how reopened requests and missing dates would be treated.
Propose a small validation example before building the view.
Explain; don’t run code.

This is a planning example, not a query for an existing dataset. Replace it with a question your team owns. Limit the first version to the minimum information needed to answer that question. When a proposed chart introduces a new metric, ask whether the audience needs it and who can confirm its meaning.

Keep a person’s approved definitions in fenbs, with the reason for any exclusion. Another assistant can then continue from the same choices instead of reverse engineering them from a chart title.

What belongs in a metric definition?

Write each definition in ordinary language before checking its SQL. A useful definition tells someone which records belong, what time means, and how the result should be interpreted. Include exceptions that could otherwise make two readers produce different answers from the same source.

Keep the record short enough to update. If a definition changes, say which earlier choice it replaces. Do not leave a metric labeled “approved” after changing the treatment of duplicates or dates; that change needs a fresh check and a clear explanation for the people who use it.

How do you validate a dashboard before sharing it?

Check the data behind a chart before judging its appearance. Start with a small set of records whose expected result you can calculate independently. Compare that result with the query and the visible view. Then test a time boundary and an exception, such as a reopened request in the support-team example.

• Compare the displayed total with the reviewed source calculation.
• Check whether joins count the same business record more than once.
• Test missing values and periods with no matching records.
• Check that labels explain the unit, time period, and important exclusions.
• If the view includes a filter, test the expected result with and without that filter.
• Ask another reader to explain the view without your narration; correct ambiguous wording.

Record failed checks as carefully as successful ones. “Looks right” gives the next reviewer nothing to reproduce. A useful result says what was compared, what matched, and what remains untested. Keep a suggested explanation separate from a confirmed reason until someone has checked the underlying records.

What access and execution limits should you check?

Genie Code can run code and use permitted data; its default classifier approves tool actions within a request, and users can choose their own approvals. Relevant authorized context can be sent to the model, including schema information, code, samples, and outputs. Read the Genie Code requirements↗ (opens in a new tab) before making a broad build request.

Agentic capabilities require partner-powered AI features at account and workspace levels and a supported region. These sources cover Databricks on AWS; they do not establish identical menus or access for every cloud and region. Ask an administrator about missing capabilities. For your own review, check the intended audience’s authorized view rather than treating your successful test as proof of everyone else’s access.

How to keep dashboard definitions with fenbs

Keep the work so you can carry on from exactly where you left off, on any device and in another AI. Save the approved meaning and the checks, with a link to the actual dashboard. fenbs does not build the dashboard or calculate its metrics; it fits when the review and next steps need to survive beyond one conversation.

1. Create a free account.
2. Write down what the view answers, what each number means, and where the dashboard lives.
3. Save who approved the definitions, what you checked, and any question still waiting for an answer.
4. Connect Databricks to fenbs with help from the person who manages your workspace.
5. Before stopping, save the next check. Ask the next connected assistant to read the saved work before changing a definition.

The Genie Code MCP guide↗ (opens in a new tab) explains its governed outside-tool support. The fenbs connection list has no Databricks-specific guide, so your workspace manager must verify and allow the setup. Supporting MCP does not make every server available to every user.

Where can you go next?

Use Databricks Assistant for notebook and SQL help, and Databricks AI functions for that separate intent. For external clients, see Databricks MCP and Claude connectors for Databricks. Databricks agent memory covers retaining work; the AI at work hub gathers more practical workflows.