https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/ba-p/164326 <P><SPAN>This blog post will resonate if you/your company fit into any of the following profiles:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Your </SPAN><STRONG>data models are stuck</STRONG><SPAN> into specific dashboards that are maintained by few specialized people.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>You </SPAN><STRONG>don't have control over your business metrics</STRONG><SPAN> because the calculations are locked into the specific dashboards or dashboard files that are not version controlled or stored into an enterprise repository or catalog.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Your </SPAN><STRONG>Data Engineering team &amp; Analysts</STRONG><SPAN> team try to </SPAN><STRONG>figure out where calculations should be made</STRONG><SPAN>, and your company ultimately doesn't know where transformations occur.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>You studied Entity Relationship Diagrams, Data Modeling, Governance, and have always aspired to have it implemented in your organization, but suffered from a great </SPAN><STRONG>number of disparate tools and processes with a lack of centralization</STRONG><SPAN> to actually have it in place.&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>You are </SPAN><STRONG>tired of paying license costs</STRONG> <STRONG>for your visualization tools</STRONG><SPAN>, and your business users barely use the dashboards your team develops due to lack of interactivity, lack of centralization, and incapability of deep diving on specific measures in a more dynamic way.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>You can't make AI “talk business”</STRONG><SPAN> because it simply doesn't have access to your data models.&nbsp;</SPAN></LI> </UL> <P><SPAN>You are a </SPAN><STRONG>strong fit for this discussion if your data is already accessible through Databricks, but you use an External BI tool</STRONG> <STRONG>for visualization</STRONG><SPAN>.&nbsp;</SPAN></P> <H2><STRONG>A quick primer: Unity Catalog and Business Semantics</STRONG></H2> <P><A href="https://www.databricks.com/product/unity-catalog" target="_blank" rel="noopener"><SPAN>Unity Catalog</SPAN></A><SPAN> is the central repository for your data, where you govern, monitor, and share it across your consumers. It governs your tables, views, functions, models, files (through Volumes), MCP servers, and </SPAN><STRONG>Business Semantics</STRONG><SPAN> too.</SPAN></P> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Databricks Unity Catalog" style="width: 804px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29886iE11BABA766130859/image-dimensions/804x225?v=v2" width="804" height="225" role="button" title="pedrozanlorensi_0-1786450556692.png" alt="Databricks Unity Catalog" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Databricks Unity Catalog</span></span></P> <P>&nbsp;<SPAN>Business Semantics has two integrated components:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Agent metadata:</STRONG><SPAN> helps AI tools interpret your data in business terms, such as synonyms, definitions, and display names.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Metric views</STRONG><SPAN>: a centralized way to define your data models: how tables should be joined, which fields matter for business analysis, how measures are calculated, common aggregations and filters, giving your end-users a trusted set of assets to pull from.</SPAN></LI> </UL> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Metric Views in the Unity Catalog" style="width: 768px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29905iE68D525ADF9A9591/image-dimensions/768x382?v=v2" width="768" height="382" role="button" title="Screenshot 2026-08-11 at 9.14.45 PM.png" alt="Metric Views in the Unity Catalog" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Metric Views in the Unity Catalog</span></span></P> <P>&nbsp;<STRONG style="color: inherit; font-size: 24px;">Why this migration matters</STRONG></P> <P><SPAN>Many organizations want to reduce the fragmentation between their data platform and their BI layer. Migrating dashboards into Databricks brings the semantic logic and the dashboard experience closer to the governed data foundation. In the Genie Code import flow, the generated dashboard is connected to metric views, and those metric views can later be promoted to Unity Catalog so they become reusable across dashboards, Genie Agents, and notebooks, with governance features such as access controls, lineage, and discoverability.</SPAN></P> <P><SPAN>That matters because a migration isn't only about copying charts. It's about preserving business logic in a reusable, governed form.</SPAN></P> <H2><STRONG>A practical migration playbook&nbsp;</STRONG></H2> <P><SPAN>Here, we detail a practical migration framework for Power BI and Tableau reports, as they are currently supported out of the box for an automated migration. The table below outlines the shape of the journey:&nbsp;</SPAN></P> <TABLE> <TBODY> <TR> <TD> <P><STRONG>Phase</STRONG></P> </TD> <TD> <P><STRONG>Steps</STRONG></P> </TD> </TR> <TR> <TD> <P><STRONG>Plan</STRONG></P> </TD> <TD> <P><SPAN>(1) Pilot&nbsp;</SPAN></P> <P><SPAN>(2) Guardrails</SPAN></P> </TD> </TR> <TR> <TD> <P><STRONG>Prepare</STRONG></P> </TD> <TD> <P><SPAN>(3) Data prep &amp; Exporting dashboards</SPAN></P> </TD> </TR> <TR> <TD> <P><STRONG>Import &amp; refine</STRONG></P> </TD> <TD> <P><SPAN>(4) Import with Genie Code&nbsp;</SPAN></P> <P><SPAN>(5) Iterate with screenshots</SPAN></P> </TD> </TR> <TR> <TD> <P><STRONG>Productionize</STRONG></P> </TD> <TD> <P><SPAN>(6) Know the coverage&nbsp;</SPAN></P> <P><SPAN>(7) Promote to Unity Catalog&nbsp;</SPAN></P> <P><SPAN>(8) Validate&nbsp;</SPAN></P> <P><SPAN>(9) Define the right permissions model for your objects and implement policies for granular access control</SPAN></P> <P><SPAN>(10) Productionize and Go Live</SPAN></P> </TD> </TR> </TBODY> </TABLE> <H3><STRONG>1. Start with a representative pilot</STRONG></H3> <P><SPAN>Choose a representative subset of dashboards that varies in complexity, so you learn where the process is smooth and where follow-up work is needed.</SPAN></P> <P><SPAN>A good pilot typically includes:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>A dashboard with straightforward measures and filters</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>A dashboard that is widely used by the business</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>A dashboard with a more complex visual layout or shared data sources</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>A dashboard with heavier calculated logic</SPAN></LI> </UL> <P><SPAN>The goal at this stage isn't scale. It is to understand migration patterns early, build confidence, and identify which dashboards will need further refinement.</SPAN></P> <H3><STRONG>2. Set guardrails for security, governance, and naming</STRONG></H3> <P><SPAN>Before preparing files or prompting Genie Code, align on the target operating model. This matters most in larger migrations, where different teams might otherwise create dashboards, metric views, and Genie assets with inconsistent permissions and naming.</SPAN></P> <P><SPAN>Set guardrails in four areas first:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Inventory and rationalization:</STRONG><SPAN> Catalog your dashboards, reports, shared models, extracts, and dependencies, so you can decide what to migrate, what to consolidate into a single target model, and what to retire.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Security and governance:</STRONG><SPAN> Decide which UC catalogs and schemas will hold migrated assets, how PII and sensitive fields should be handled, and which permissions apply to dashboards, metric views, and supporting data assets.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Credential model:</STRONG><SPAN> Decide whether access and refresh behavior should rely on shared credentials, individual user access, or a phased mix during migration; and also whether this will be different during migration for testing and at production go live.&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Naming conventions:</STRONG><SPAN> Define standard patterns for your objects, and follow your existing conventions. Schemas, metric views, dashboards, and Genie spaces, should all have a cohesive nomenclature. This way, the assets that are created as a part of the conversion process are easy to discover and operate at scale.</SPAN></LI> </UL> <P><SPAN>This is also where teams usually spot duplicate KPI logic, near-identical dashboards that can be merged, and shared semantic layers that should be migrated once and reused broadly.</SPAN></P> <P><STRONG><I>A practical tip: </I></STRONG><I><SPAN>Encode these conventions directly into your Genie Code instructions. You can tell Genie Code which catalog and schema to use, how to name assets, and when to prefer existing governed resources over creating new ones.</SPAN></I></P> <H3><STRONG>3. Export your legacy dashboards</STRONG></H3> <P><SPAN>Start by selecting reports that query from the Databricks Unity Catalog. </SPAN><SPAN>If your Power BI/Tableau reports do not query from it today, can you make it so that they can query from Databricks?&nbsp;</SPAN></P> <P><SPAN>Here are some instructions on how you can connect to the data source and make it available/visible through Unity Catalog. Pick from one or more of the following connectivity options based on your source:</SPAN></P> <TABLE> <TBODY> <TR> <TD> <P><STRONG>If your data is…</STRONG></P> </TD> <TD> <P><STRONG>Use</STRONG></P> </TD> <TD> <P><STRONG>What it does</STRONG></P> </TD> </TR> <TR> <TD> <P><SPAN>In a source system, and you want to ingest this to the lakehouse</SPAN></P> </TD> <TD> <P><A href="https://docs.databricks.com/aws/en/ingestion/overview" target="_blank" rel="noopener"><STRONG>Lakeflow Connect</STRONG></A></P> </TD> <TD> <P><SPAN>Ingests from enterprise apps, databases, cloud storage, message buses, and local files into Unity Catalog.</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Must stay where it lives</SPAN></P> </TD> <TD> <P><A href="https://docs.databricks.com/aws/en/query-federation/" target="_blank" rel="noopener"><STRONG>Lakehouse Federation</STRONG></A></P> </TD> <TD> <P><SPAN>Makes it visible from Unity Catalog without moving it. Watch latency on large inputs as queries still run against the source.</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Just static files</SPAN></P> </TD> <TD> <P><A href="https://docs.databricks.com/aws/en/volumes/" target="_blank" rel="noopener"><STRONG>Volumes</STRONG></A></P> </TD> <TD> <P><SPAN>Makes the files visible from Unity Catalog.</SPAN></P> </TD> </TR> </TBODY> </TABLE> <P><SPAN>Once your data is reachable, export your dashboards in two quick steps.</SPAN></P> <P><STRONG>Step 1: Export from your BI tool</STRONG></P> <TABLE> <TBODY> <TR> <TD> <P><STRONG>From</STRONG></P> </TD> <TD> <P><STRONG>Export as</STRONG></P> </TD> <TD> <P><STRONG>Reference</STRONG></P> </TD> </TR> <TR> <TD> <P><STRONG>Tableau</STRONG></P> </TD> <TD> <P><SPAN>.twb, .twbx, .tds, or .tdsx</SPAN></P> </TD> <TD> <P><SPAN>Check out the Tableau </SPAN><A href="https://help.tableau.com/current/pro/desktop/en-us/export.htm" target="_blank" rel="noopener"><SPAN>documentation</SPAN></A><SPAN> for more information.</SPAN></P> </TD> </TR> <TR> <TD> <P><STRONG>Power BI</STRONG></P> </TD> <TD> <P><SPAN>.pbit</SPAN></P> </TD> <TD> <P><SPAN>Check out this </SPAN><A href="https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-templates" target="_blank" rel="noopener"><SPAN>documentation</SPAN></A><SPAN> to learn more about this format. Make sure that you export the .pbit from the reports that were used to generate your semantic model in the first place from Power BI Desktop.&nbsp;</SPAN></P> </TD> </TR> </TBODY> </TABLE> <P><STRONG>Step 2: Save your exports to a Volume in Unity Catalog</STRONG></P> <P><SPAN>Move the exported files to a Unity Catalog volume. Genie Code can import directly from there, and it's required for any file larger than 100 MB.&nbsp;</SPAN></P> <P><SPAN>Note that if your report file has less than 100 MB, you can directly attach it to the Genie Code chat instead of uploading it to a Volume, or use the </SPAN><I><SPAN>Import dashboard from file</SPAN></I><SPAN> approach outlined in the next section.&nbsp;</SPAN></P> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Upload your dashboard files to a Volume in the Unity Catalog" style="width: 743px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29884iA0556F401EB9A93B/image-dimensions/743x289?v=v2" width="743" height="289" role="button" title="pedrozanlorensi_2-1786450556693.png" alt="Upload your dashboard files to a Volume in the Unity Catalog" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Upload your dashboard files to a Volume in the Unity Catalog</span></span></P> <H3><STRONG>4. Import the dashboard with Genie Code</STRONG></H3> <P><STRONG>Pre-requisites: </STRONG><SPAN>Please validate</SPAN><A href="https://docs.databricks.com/aws/en/dashboards/manage/import-bi#requirements" target="_blank" rel="noopener"><SPAN> these requirements</SPAN></A><SPAN> before proceeding with the migration.</SPAN></P> <P><SPAN>There are two ways to start the import:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>From the Dashboards page</STRONG><SPAN>: click </SPAN><STRONG>Create</STRONG><SPAN> and choose </SPAN><I><SPAN>Import dashboard from file</SPAN></I><SPAN>.</SPAN></LI> </UL> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Import dashboard from file" style="width: 458px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29888iEE2A93F4C1323595/image-dimensions/458x259?v=v2" width="458" height="259" role="button" title="pedrozanlorensi_3-1786450556693.png" alt="Import dashboard from file" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Import dashboard from file</span></span>&nbsp;</P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>From a draft dashboard</STRONG><SPAN>: switch Genie Code to Agent mode and run </SPAN><STRONG>/importBI</STRONG><SPAN> with your file or volume path.</SPAN></LI> </UL> <P><SPAN>The hands-on flow would be:</SPAN></P> <OL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Go to </SPAN><STRONG>Dashboards → Create dashboard</STRONG><SPAN>.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Open the Genie Code panel (</SPAN><span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="pedrozanlorensi_4-1786450556693.png" style="width: 17px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29887i4BB2D479810376EE/image-dimensions/17x17?v=v2" width="17" height="17" role="button" title="pedrozanlorensi_4-1786450556693.png" alt="pedrozanlorensi_4-1786450556693.png" /></span><SPAN>, top-right) and start a </SPAN><STRONG>new</STRONG><SPAN> chat with the pencil icon (a fresh chat gives the best experience).</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Type </SPAN><STRONG>/importBI</STRONG><SPAN> and paste the Volume path to your first dashboard.</SPAN></LI> </OL> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Using /importBI through Genie Code" style="width: 711px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29889i1201AC14AE2CA794/image-dimensions/711x344?v=v2" width="711" height="344" role="button" title="pedrozanlorensi_5-1786450556694.png" alt="Using /importBI through Genie Code" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Using /importBI through Genie Code</span></span></P> <P>&nbsp;<SPAN>From here, Genie Code walks you through the migration:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Inspects your model</STRONG><SPAN> and asks where target views and metric views should live.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Proposes a plan</STRONG><SPAN> - identifies fact vs. dimension tables, reviews your metric definitions, and flags any formulas with no direct match. Review it, give feedback on anything you'd change, then approve.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Builds the objects</STRONG><SPAN> in your target catalog and schema once you confirm.</SPAN></LI> </UL> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Genie Code confirming the target catalog and schema for the Metric Views" style="width: 448px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29891iD254C6163B7EB364/image-dimensions/448x556?v=v2" width="448" height="556" role="button" title="pedrozanlorensi_6-1786450556694.png" alt="Genie Code confirming the target catalog and schema for the Metric Views" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Genie Code confirming the target catalog and schema for the Metric Views</span></span></P> <P>&nbsp;<STRONG style="font-family: inherit;">Key considerations</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Expect more than one object.</STRONG><SPAN> The output usually isn't a single metric view, but a mix of metric views and views. Both Power BI and Tableau pack several transformation steps into their modeling and visualization layers, and splitting those into distinct objects keeps the semantic layer clean.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>If you already have shared KPI logic in Unity Catalog, say so in the prompt.&nbsp; The import will reference your existing metric views, or extend one when needed. Always be explicit about the catalog and schema you want as both source and target.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Example prompts</STRONG><SPAN>:&nbsp;</SPAN></LI> <UL> <LI style="font-weight: 400;" aria-level="2"><SPAN>"</SPAN><STRONG>/importBI</STRONG><SPAN> &lt;volume path&gt;, and recreate the layout as closely as possible using the attached screenshot as the visual reference."</SPAN></LI> <LI style="font-weight: 400;" aria-level="2"><SPAN>"</SPAN><STRONG>/importBI</STRONG><SPAN> &lt;volume path&gt;, and save the converted views/metric views to my schema &lt;catalog_name&gt;.&lt;schema_name&gt;."</SPAN></LI> <LI style="font-weight: 400;" aria-level="2"><SPAN>"</SPAN><STRONG>/importBI</STRONG><SPAN> &lt;volume path&gt;, and save the converted views/metric views to my schema &lt;catalog_name&gt;.&lt;schema_name&gt;, using the same nomenclature standards we already use, with views starting with v_ and metric views starting with metricv_."</SPAN></LI> <LI style="font-weight: 400;" aria-level="2"><SPAN>"</SPAN><STRONG>/importBI</STRONG><SPAN> Import the attached report and prefer existing Unity Catalog metric views from &lt;catalog.schema path&gt; for shared KPIs such as revenue, gross margin, and active customers. If an equivalent governed metric view does not exist, create dashboard-local metric views and identify which ones should be promoted later."</SPAN></LI> </UL> </UL> <H3><STRONG>5. Improve fidelity with screenshots and iteration</STRONG></H3> <P><SPAN>Treat migration as an iterative workflow, not a one-shot conversion. Expect to prompt again after the first pass. Work through the migration plan step by step, checking whether each pattern is converted correctly and adding clues where one needs more guidance.</SPAN></P> <P><STRONG>Start with the semantic layer</STRONG><SPAN>: If your report depends on a shared Tableau data source or a shared Power BI model, migrate the semantic layer first, before recreating the visuals.</SPAN></P> <P><SPAN>Example prompt:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>"</SPAN><STRONG>/importBI</STRONG><SPAN> &lt;volume path&gt; Use the shared model definitions I uploaded, create the target views and metric views in &lt;catalog&gt;.&lt;schema&gt;, and focus on the semantic model first. Do not recreate visualizations yet"</SPAN></LI> </UL> <P><STRONG>Then build the visuals:</STRONG><SPAN> When you refine the visuals, screenshots of the original dashboard still help improve layout and formatting fidelity.</SPAN></P> <P><STRONG>Example prompts</STRONG><SPAN>:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Adjust this imported dashboard so the layout more closely matches the attached source screenshot, especially the KPI row, the left-hand filters, and the summary chart ordering."</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Review this migrated dashboard and fix any missing or incorrectly mapped filters. If a direct equivalent is not possible, use the closest supported interaction and explain the gap."</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Refine the imported metric views and field labels so the business names, display formats, and comments are consistent with the original report and our Unity Catalog standards."</SPAN></LI> </UL> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Instructing Genie Code using Screenshots" style="width: 527px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29890i1D895C288BD81440/image-dimensions/527x141?v=v2" width="527" height="141" role="button" title="pedrozanlorensi_7-1786450556694.png" alt="Instructing Genie Code using Screenshots" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Instructing Genie Code using Screenshots</span></span></P> <P><I><SPAN>A common mistake here is a single generic prompt like "make it better." You'll get more reliable results by asking for one class of adjustment at a time: layout, filters, calculations, formatting, or field naming.</SPAN></I></P> <H3><STRONG>6. Understand what gets migrated well</STRONG></H3> <P><SPAN>The flow covers a meaningful slice of typical BI assets out of the box, but a few things need a human eye. Here are some insights based on multiple migrations executed by the Databricks team:</SPAN></P> <TABLE> <TBODY> <TR> <TD> <P><STRONG>Migrates well</STRONG></P> </TD> <TD> <P><STRONG>Watch for</STRONG></P> </TD> </TR> <TR> <TD> <P><SPAN>Calculated fields and measures</SPAN></P> </TD> <TD> <P><SPAN>Chart types match only where a direct equivalent exists</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Data source connections</SPAN></P> </TD> <TD> <P><SPAN>No equivalent? It may fall back to a bar chart</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Charts and visualizations</SPAN></P> </TD> <TD> <P><SPAN>Widget positioning is approximate: expect layout tweaks</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Dashboard- and page-level filters</SPAN></P> </TD> <TD> <P><SPAN>Some level-of-detail expressions and drill-down behavior may not be fully covered yet</SPAN></P> </TD> </TR> </TBODY> </TABLE> <P><SPAN>Refer to the </SPAN><A href="https://docs.databricks.com/aws/en/ai-bi/release-notes/2026" target="_blank" rel="noopener"><SPAN>release notes</SPAN></A><SPAN> for the latest feature coverage</SPAN></P> <H3><STRONG>7. Plan for Unity Catalog metric views in production</STRONG></H3> <P><SPAN>After import, Genie Code creates metric views for your measures and dimensions, local to the dashboard by default, which is ideal while you iterate. Point it at a specific catalog (with the right permissions) and it can create them directly in Unity Catalog instead.</SPAN></P> <P><SPAN>For production, promote local metric views to Unity Catalog so they're reusable across dashboards, Genie Agents, and notebooks under central governance.&nbsp;</SPAN></P> <P><SPAN>This is the step that turns a dashboard migration into a governed analytics foundation, not just a dashboard-only conversion.</SPAN></P> <H3><STRONG>8. Validate before broad rollout</STRONG></H3> <P><SPAN>Before you scale migration across a larger estate, validate a few core areas carefully:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Are the metrics and dimensions producing the expected numbers?</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Do filters behave the way end users expect?</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Are charts mapped appropriately, especially where there's no direct visual equivalent?</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Does the layout need manual adjustment for usability?</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Are your most important dashboards backed by promoted metric views in Unity Catalog?</SPAN></LI> </UL> <P><SPAN>This final review is what separates a technically successful import from a truly production-ready rollout.</SPAN></P> <P><STRONG>Ready-to-copy validation prompts</STRONG><SPAN>:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Compare this migrated dashboard to the source/screenshot and list any gaps in filters, KPIs, formats, or layout."</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Check whether this migrated report is production-ready. Focus on KPI logic, filter behavior, labels, and formatting."</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>"Validate whether this report reused the correct Unity Catalog metric views and flag any local metric views that should be promoted."</SPAN></LI> </UL> <H3><STRONG>9. Define the right permissions model for your objects and implement policies for granular access control</STRONG></H3> <P><SPAN>To secure access to your objects and establish granular permissions, Databricks allows you to manage permissions and to implement policies at the Unity Catalog level using Row-Level Security (RLS) and Column-Level Masking (CLM).&nbsp;</SPAN></P> <P><SPAN>Learn more about these concepts and make sure to implement them so that your semantic model is well governed:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><A href="https://docs.databricks.com/aws/en/data-governance/unity-catalog/access-control/" target="_blank" rel="noopener"><SPAN>Access control in Unity Catalog</SPAN></A></LI> <LI style="font-weight: 400;" aria-level="1"><A href="https://docs.databricks.com/aws/en/data-governance/unity-catalog/access-control/privileges-reference" target="_blank" rel="noopener"><SPAN>Unity Catalog privileges reference</SPAN></A><SPAN>&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><A href="https://docs.databricks.com/aws/en/data-governance/unity-catalog/filters-and-masks/" target="_blank" rel="noopener"><SPAN>Row filters and column masks</SPAN></A></LI> <LI style="font-weight: 400;" aria-level="1"><A href="https://docs.databricks.com/aws/en/data-governance/unity-catalog/abac/" target="_blank" rel="noopener"><SPAN>Attribute-based access control in Unity Catalog</SPAN></A><SPAN>&nbsp;</SPAN></LI> </UL> <P><SPAN>And note that when you publish your dashboards in AI/BI, you get to choose whether your users will all see the same thing (through shared data permissions), or they will only see what they are entitled to see, as per their permissions in the Unity Catalog (individual data permission).</SPAN></P> <P><SPAN>Learn more about publishing and sharing your dashboards here: </SPAN><A href="https://docs.databricks.com/aws/en/dashboards/share/share" target="_blank" rel="noopener"><SPAN>Share a dashboard</SPAN></A><SPAN>.&nbsp;&nbsp;</SPAN></P> <H3><STRONG>10. Polish, productionize and go live</STRONG></H3> <P><SPAN>No migration tool is a silver bullet, so expect some final tweaks even after Genie Code's back-and-forth: matching your business or dev standards, visual and color updates, filter or parameter changes, custom logic, or materializations for speed.</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>As you productionize, use </SPAN><STRONG>Declarative Automation Bundles</STRONG><SPAN> to version dashboard changes and enable CI/CD.</SPAN></LI> </UL> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Don't stop at chart parity.</STRONG><SPAN> Make sure the dashboard is Genie-enabled so users can ask questions the fixed visuals don't answer. Published dashboards include a Genie Agent by default (the Enable Genie toggle is on when you publish). If that default agent is enough, just verify it has the right datasets and permissions. If users need broader ad-hoc exploration, cross-dashboard context, or exception/drill-around analysis, link or deploy a separate Genie Agent alongside&nbsp; and since Genie Agents work even better with metric views, all your migration work carries straight over. See </SPAN><A href="https://docs.databricks.com/aws/en/genie-agents/best-practices" target="_blank" rel="noopener"><STRONG>Curate an Effective Genie Agent</STRONG></A><I><SPAN>.</SPAN></I></LI> </UL> <P><STRONG>Ready-to-copy Genie prompt</STRONG><SPAN>:</SPAN></P> <P><I><SPAN>"Ensure this migrated dashboard is Genie-enabled. If the default companion Genie Agent is too narrow, recommend whether I should link an existing Genie Agent or create a separate one for ad-hoc business questions beyond the dashboard visuals”.</SPAN></I></P> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Enable Genie for your AI/BI Dashboards" style="width: 759px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29892iECE1B79DC9D77FDD/image-dimensions/759x371?v=v2" width="759" height="371" role="button" title="pedrozanlorensi_8-1786450556694.png" alt="Enable Genie for your AI/BI Dashboards" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Enable Genie for your AI/BI Dashboards</span></span></P> <P><span class="lia-inline-image-display-wrapper lia-image-align-center" image-alt="Dashboard users can talk to Genie while examining the charts to get detailed insights" style="width: 749px;"><img src="https://community.databricks.com/t5/image/serverpage/image-id/29893i37D28D37239CCBE8/image-dimensions/749x384?v=v2" width="749" height="384" role="button" title="pedrozanlorensi_9-1786450556695.png" alt="Dashboard users can talk to Genie while examining the charts to get detailed insights" /><span class="lia-inline-image-caption" onclick="event.preventDefault();">Dashboard users can talk to Genie while examining the charts to get detailed insights</span></span></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>You can also define a custom skill in Genie Code that captures your formatting defaults — colors, fonts, starting page, and so on.</SPAN></LI> </UL> <P><STRONG>Ready-to-copy instruction prompt</STRONG><I><SPAN>:</SPAN></I></P> <P><I><SPAN>"Add to instruction that whenever I create or populate a new dashboard, The Genie Code should automatically add a first canvas page called "About" containing a text widget with the dashboard description, an image widget placeholder, and the owner's name as a mailto: markdown link. The widget summarizing the dashboard's purpose should include the brief summary for each page with hyperlinks"</SPAN></I></P> <P><SPAN>Refer to these for advanced customizations:</SPAN></P> <TABLE> <TBODY> <TR> <TD> <P><STRONG>To</STRONG></P> </TD> <TD> <P><STRONG>Reference</STRONG></P> </TD> </TR> <TR> <TD> <P><SPAN>Apply advanced metric view patterns</SPAN></P> </TD> <TD> <P><SPAN>Advanced Metric view patterns: </SPAN><A href="https://docs.databricks.com/aws/en/uc-semantics/metric-views/advanced-techniques" target="_blank" rel="noopener"><SPAN>https://docs.databricks.com/aws/en/uc-semantics/metric-views/advanced-techniques</SPAN></A></P> <P><SPAN>Level of Detail expressions &nbsp; </SPAN><A href="https://docs.databricks.com/aws/en/uc-semantics/metric-views/level-of-detail" target="_blank" rel="noopener"><SPAN>https://docs.databricks.com/aws/en/uc-semantics/metric-views/level-of-detail</SPAN></A></P> <P><SPAN>Reference measures repo </SPAN><A href="https://github.com/databricks-solutions/uc-semantics-patterns" target="_blank" rel="noopener"><SPAN>https://github.com/databricks-solutions/uc-semantics-patterns</SPAN></A></P> </TD> </TR> <TR> <TD> <P><SPAN>Customize color palette</SPAN></P> </TD> <TD> <P><SPAN>Design Beautiful Dashboards in AI/BI (</SPAN><A href="https://www.databricks.com/blog/design-beautiful-dashboards-aibi" target="_blank" rel="noopener"><SPAN>https://www.databricks.com/blog/design-beautiful-dashboards-aibi</SPAN></A><SPAN>)</SPAN></P> </TD> </TR> <TR> <TD> <P><SPAN>Build custom visuals with VegaLite</SPAN></P> </TD> <TD> <P><SPAN>Create custom visuals: </SPAN><A href="https://docs.databricks.com/aws/en/dashboards/manage/visualizations/custom-visualizations" target="_blank" rel="noopener"><SPAN>https://docs.databricks.com/aws/en/dashboards/manage/visualizations/custom-visualizations</SPAN></A></P> <P><SPAN>VegaLite examples: </SPAN><A href="https://vega.github.io/vega-lite/examples/" target="_blank" rel="noopener"><SPAN>https://vega.github.io/vega-lite/examples/</SPAN></A></P> </TD> </TR> <TR> <TD> <P><SPAN>Speed up common aggregations&nbsp;</SPAN></P> </TD> <TD> <P><SPAN>Materialize Metric Views: </SPAN><A href="https://docs.databricks.com/aws/en/uc-semantics/metric-views/materialization" target="_blank" rel="noopener"><SPAN>https://docs.databricks.com/aws/en/uc-semantics/metric-views/materialization</SPAN></A></P> </TD> </TR> </TBODY> </TABLE> <H2><STRONG>Conclusion</STRONG></H2> <P><SPAN>Migrating dashboards to Databricks AI/BI does not have to be a long, manual conversion project.&nbsp;</SPAN></P> <P><SPAN>As BI tools continue to evolve, organizations will need to migrate their older reports and dashboards.&nbsp; The questions that need to be answered while deciding the best way forward are:&nbsp;</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Does the migration create fragmentation between BI assets and the data estate, or does it unify it?&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Can you model data and governance once leveraging Unity Catalog or does this have to be repeated across systems resulting in admin overhead?&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Are you still going to have to pay for user license fees?&nbsp;</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Can your users interact with your new reports &amp; dashboards using natural-language?</SPAN></LI> </UL> <P><SPAN>The product is evolving quickly, and the migration target keeps strengthening as AI/BI and Business Semantics add richer capabilities.</SPAN></P> <P><SPAN>So treat your migration as an iterative modernization effort, not a static task. Start with the fastest path to a trusted dashboard, reuse governed assets where possible, and keep refining the semantic layer as new capabilities arrive.</SPAN></P> <P><SPAN>Our platform is moving really fast, and techniques that still need workarounds today may become first-class migration patterns by the time your next wave lands. Stay tuned!</SPAN></P> Wed, 12 Aug 2026 11:42:41 GMT pedrozanlorensi 2026-08-12T11:42:41Z https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/ba-p/164326 <P>Learn how to use Genie Code to migrate your Dashboards to Databricks AI/BI!</P> Wed, 12 Aug 2026 11:42:41 GMT https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/ba-p/164326 pedrozanlorensi 2026-08-12T11:42:41Z https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/bc-p/165510#M1169 <P>Great structured playbook — the 10-step framing makes this approachable for teams that are intimidated by "migration" as a project.</P><P>&nbsp;</P><P>One thing worth calling out for larger migrations: the serverless compute Genie Code spins up during /importBI runs is billed under your workspace's serverless budget, not tied to a specific project or cost center by default. If you're migrating 30+ dashboards in parallel across multiple teams, that attribution gap adds up fast and can surprise finance at month-end.</P><P>&nbsp;</P><P>A practical guardrail before starting: set a serverless budget policy scoped by tag or user group, so you get early signals on spend per migration workstream rather than discovering it after the fact.</P><P>&nbsp;</P><P>On the Unity Catalog promotion step (step 7) — for teams in regulated industries (banking, healthcare), this is also where a PII scan of migrated metric views pays off. Genie Code faithfully migrates computed columns and filters, but it won't flag if a measure accidentally surfaces personal data that was previously hidden by row-level security in the original BI layer. Worth a data governance review pass before broad publication, especially if your Unity Catalog has mixed sensitivity tiers.</P> Wed, 12 Aug 2026 14:35:34 GMT https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/bc-p/165510#M1169 DoTA 2026-08-12T14:35:34Z https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/bc-p/165577#M1172 <P><a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/125801">@DoTA</a>&nbsp;- Great input on the Serverless Budget Policy. I recommend checking out this doc for a reference on how to manage budgets and cost controls for Genie:&nbsp;<A href="https://docs.databricks.com/aws/en/genie/budgets" target="_blank">https://docs.databricks.com/aws/en/genie/budgets</A></P> <P>Additionally, step 9 covers UC permissions and policies review, which is super important indeed.&nbsp;</P> Wed, 12 Aug 2026 20:52:34 GMT https://community.databricks.com/t5/technical-blog/migrate-your-dashboards-to-ai-bi-with-genie-code/bc-p/165577#M1172 pedrozanlorensi 2026-08-12T20:52:34Z