Drill-down support in Databricks SQL (Lakeview) Dashboards

vamsi_simbus — Mon, 06 Apr 2026 09:37:28 GMT

Hi All,

Does Databricks SQL (Lakeview) Dashboards support native drill-down functionality (for example: Category → Subcategory → SKU)?

Currently, we see support for cross-filtering, parameters, and drill-through within the same dataset, but hierarchical drill paths seem limited compared to traditional BI tools.

Would like to understand:

Current best practices to implement drill-down
Known limitations
Any roadmap for enhanced drill-down capabilities

Thanks in advance!

Re: Drill-down support in Databricks SQL (Lakeview) Dashboards

Sumit_7 — Mon, 06 Apr 2026 11:56:46 GMT

No support for native hierarchical drill-down. Best practice would be to Drill-through pages, Schema modeling or an Optional BI tool. Check out the below resources:
~ https://docs.databricks.com/en/dashboards/
~ https://docs.databricks.com/en/dashboards/manage/filters/

Re: Drill-down support in Databricks SQL (Lakeview) Dashboards

anuj_lathi — Wed, 08 Apr 2026 11:10:06 GMT

Hi — good question. You're right that Lakeview doesn't have native hierarchical drill-down (click Category → auto-expand to Subcategory → SKU). But you can get fairly close by combining the features you mentioned. Here are the practical patterns:

1. Cross-Filtering as Pseudo Drill-Down

Cross-filtering lets viewers click a data point in one chart and all other visualizations on the same dataset update automatically. You can simulate drill-down by placing charts at different granularity levels on the same page:

Chart 1: Bar chart by Category
Chart 2: Bar chart by Subcategory
Chart 3: Table by SKU

When a viewer clicks "Electronics" in Chart 1, Charts 2 and 3 automatically filter to show only Electronics subcategories and SKUs. This gives a drill-down feel without leaving the page.

Supported chart types for cross-filtering: bar, box plot, heatmap, histogram, pie, scatter, and point map.

2. Drill-Through Pages (Overview → Detail)

Set up a multi-page dashboard:

Page 1 (Overview): Aggregated view by Category (bar/pie chart)
Page 2 (Details): Detailed view with Subcategory and SKU breakdowns, with a field filter matching the Category column

Viewers right-click a segment on Page 1 and select "Drill to Details" — Page 2 opens with the filter auto-populated. This is the closest to traditional BI drill-down.

Setup: Add a filter widget on the target page whose field type matches the source chart's data type.

3. Parameter-Driven Granularity (Dynamic SQL)

Use a parameter to let users choose the aggregation level dynamically. For example, to drill through date hierarchies:

SELECT

DATE_TRUNC(:date_granularity, order_date) AS period,

SUM(revenue) AS total_revenue

FROM orders

GROUP BY 1

Add a dropdown filter widget bound to the :date_granularity parameter with values like year, quarter, month, week. Users switch granularity on-the-fly. The same pattern works for categorical hierarchies using CASE expressions:

SELECT

CASE :level

WHEN 'Category' THEN category

WHEN 'Subcategory' THEN subcategory

WHEN 'SKU' THEN sku

END AS dimension,

SUM(revenue) AS total_revenue

FROM products

GROUP BY 1

Known Limitations

No automatic hierarchical expansion (click-to-expand within a single chart)
Cross-filtering is one-level — clicking a filtered chart doesn't cascade further
Drill-through navigates to a new page rather than expanding in-place
No breadcrumb trail or "back" navigation between drill levels

Recommendation

For your Category → Subcategory → SKU use case, I'd combine approaches 1 + 2: use cross-filtering on an overview page with side-by-side charts at each level, and add drill-through to a detail page with a full SKU-level table. This covers most interactive exploration needs without leaving the Databricks platform.

If you need full hierarchical drill-down with expand/collapse within a single visual, you'd need to connect an external BI tool (Power BI, Tableau) via Partner Connect.

Docs:

topic Re: Drill-down support in Databricks SQL (Lakeview) Dashboards in Data Engineering

Drill-down support in Databricks SQL (Lakeview) Dashboards

Re: Drill-down support in Databricks SQL (Lakeview) Dashboards

Re: Drill-down support in Databricks SQL (Lakeview) Dashboards

1. Cross-Filtering as Pseudo Drill-Down

2. Drill-Through Pages (Overview → Detail)

3. Parameter-Driven Granularity (Dynamic SQL)

Known Limitations

Recommendation