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Business Intelligence

A well-architected BI integration on Databricks anchors on governed access through Unity Catalog, high-performance serving with SQL Warehouses, and consistent semantics via UC Metric Views and Table metadata. Partners should also adopt:

SQL serving layer​

Requirements​

  • Use of Databricks SQL warehouses as the dedicated BI serving engine as it is purpose built to provide scalable, low-latency, high-concurrency performance.
  • An option for query and predicate pushdown.
  • Graceful handling of extended startup times of classic warehouses (i.e your application shouldn't timeout in error with your default connection settings).
  • Recommend serverless warehouses over classic warehouses in your documentation.

Documentation: SQL Warehouse Types

Best practices​

  • Use Databricks drivers for managed large result retrieval and caching (CloudFetch).

Documentation: CloudFetch | Query Caching

Supported but not preferred​

  • SQL Execution API or SDKs require explicit handling for large result retrieval via EXTERNAL_LINKS.
  • For asynchronous execution, consider the JDBC driver instead.

Documentation: SQL Execution API | JDBC Driver

Anti-patterns​

  • Keeping a warehouse warm has serious cost implications. Use the Warehouse API to check state instead.

Documentation: Warehouse API

Python code execution​

Python execution needs fall into two patterns: interactive (latency-sensitive) and non-interactive (latency-tolerant).

Requirements​

Interactive use cases​

  • Use Databricks Connect for Python with serverless compute for on-demand execution where users expect responsive results.

Non-interactive use cases​

  • Use the Jobs API with serverless compute for asynchronous execution where latency is acceptable.

Documentation: Databricks Connect | Jobs API

Not eligible for validation​

  • Command Execution API only supports classic compute with long start-up times.

Centralized data governance & metadata consumption​

Treat Unity Catalog as the single semantic and descriptive source of truth for modeling and exploration.

Requirements​

  • Adopt Unity Catalog as the authoritative layer for permissions, identities, auditing, lineage, and data governance through U2M OAuth.
  • For long-lived orchestration tasks such as scheduled extracts, use Service Principal OAuth.

Best practices​

  • Token Federation and Workload Identity Federation are the preferred OAuth patterns.
  • Use table/column definitions, primary/foreign key relationships, and constraints to drive optimized logical data modeling.
  • Read table and column comments to surface business-friendly labels, tooltips, and documentation in your UI.
  • Consume tags for domains, PII/sensitivity, regulatory flags, and "certified" or "gold" indicators.

Documentation: Authentication and Authorization | Metadata & Access Control

Centrally governed business semantics​

Unity Catalog metric views require special handling and explicit configuration in the driver connection for metadata reads.

Requirements​

Eliminate semantic drift through the following practices:

  • Read metric view metadata through driver function calls, SQL SHOW/DESCRIBE commands, or Information Schema.
  • Make measures and attributes available in their native data type.
  • Compile queries using Databricks MEASURE function for performance-optimized reads with dynamic materializations.

Best practices​

  • Push updates to metric views through Data Engineering practices.

Documentation: Metric Views

Workload attribution​

Best practices​

  • Allow customers to define query tags for each warehouse connection or dashboard for operational visibility and cost attribution.

Documentation: Query Tags

Partner-provided lineage​

Best practices​

  • Create external metadata objects and relationships for your platform and dashboards using the External Metadata API for end-to-end analytical traceability.

Documentation: Data Lineage | External Lineage

AI-extended BI functions​

AI Functions are built-in Databricks SQL functions that call LLMs and ML models directly in queries for NLQ, summarization, and reasoning.

Best practices​

  • Wrap and expose Databricks AI Functions through your platform's functions for NLQ, summarization, forecasting, and automated insights within existing workflows.

Documentation: AI Functions | AI Platform

End-to-end agentic AI​

Genie provides user-defined spaces containing data assets and instructions, enabling natural language queries without SQL.

note

This section covers natural-language analytics for BI tools using Genie. For partners building standalone AI agent products, see the Enterprise search & AI agent integrations validation requirements, which require Managed MCP integration.

Best practices​

  • Extend the BI experience by integrating with Genie.
  • List available Genie spaces through the API so the BI tool can surface and invoke them.
  • Programmatically create spaces via the API when one doesn't exist for a dataset.
  • Display a "Powered by Genie" attribution label whenever Genie-generated content appears in your product.
  • Include a citation linking to the source Genie Space so users can navigate to it directly.

Documentation: Genie Spaces | Genie API | Genie Attribution

What's next​