databricks-aibi-dashboards

Name: databricks-aibi-dashboards
Availability: InStock
Author: aaronachermann

Build and deploy Databricks AI/BI dashboards with mandatory SQL validation.

Updated Apr 18, 2026

Search Tags:#validation #dashboard #data-visualization #lakehouse #databricks #sql-testing

Ready to run

npx skills add https://github.com/aaronachermann/PolentaEncoders --skill databricks-aibi-dashboards-aaronachermann

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-aibi-dashboards
Source: https://github.com/aaronachermann/PolentaEncoders/tree/main/.github/skills/databricks-aibi-dashboards
Command: npx skills add https://github.com/aaronachermann/PolentaEncoders --skill databricks-aibi-dashboards-aaronachermann

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Databricks AI/BI dashboards often suffer from deployment-time errors caused by untested SQL queries and misconfigurations; this Skill enforces a strict validation workflow to ensure dashboards are built on correct, tested queries before deployment.

Core Features & Use Cases

End-to-end dashboard creation, testing, and deployment using Lakeview/Databricks components.
Mandatory validation steps: schema inspection, query testing via execute_sql, JSON dashboard construction, and deployment via manage_dashboard.
Use cases include building performance dashboards, operational dashboards, and multi-dataset dashboards with global or page-level filters.

Quick Start

Test all SQL queries for each dataset with execute_sql, then assemble the dashboard JSON and deploy it with manage_dashboard.

Frequently Asked Questions about databricks-aibi-dashboards

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent deployment errors when building Databricks AI/BI dashboards?▼

To prevent Databricks AI/BI dashboard deployment errors, you must enforce a strict validation workflow that tests SQL queries and schemas before assembling the dashboard JSON and deploying. This ensures dashboards are built on correct, verified queries.

What is the step-by-step process to deploy a Lakehouse dashboard with SQL testing?▼

The step-by-step Lakehouse dashboard deployment process involves four mandatory steps: extract schemas, write and verify SQL queries using execute_sql, assemble the dashboard JSON, and deploy using manage_dashboard. This ensures fully tested data visualization.

Can I build multi-dataset Databricks dashboards with global filters using this validation workflow?▼

Yes, this validation workflow supports building multi-dataset Databricks dashboards with global or page-level filters. It validates underlying SQL queries across multiple datasets before you assemble and deploy the final dashboard configuration.

Why does my Databricks dashboard deployment fail due to untested SQL queries?▼

Databricks dashboard deployments fail when untested SQL queries contain misconfigurations or schema mismatches. Running a mandatory validation step to test all SQL queries via execute_sql before deployment catches these errors and ensures dashboard stability.

What's the best way to validate SQL queries before deploying a data visualization dashboard?▼

The best way to validate SQL queries for data visualization dashboards is to inspect schemas and run execute_sql to test each dataset's queries. After successful SQL testing, you can safely assemble the dashboard JSON and deploy it.