The Top 10 Best Practices for AI/BI Dashboards Performance Optimization (Part 1)

2026-02-04 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

This article, "The Top 10 Best Practices for AI/BI Dashboards Performance Optimization (Part 1)", provides a practical playbook for optimizing Databricks AI/BI dashboards to ensure consistent speed and scalability. It addresses performance issues stemming from dashboard design, warehouse configuration, and Lakehouse data patterns, offering a holistic approach rather than isolated optimizations. The guide details how a dashboard interaction flows from the browser through the AI/BI orchestration layer, Databricks SQL admission and caching, and down to file scanning in the Lakehouse. Key optimizations covered include organizing dashboards into pages, using smart defaults for filters to improve "first paint" time, leveraging parameters to slice large datasets efficiently, maximizing browser caching for small datasets, and strategically utilizing Databricks SQL's result cache, especially the remote cache in Serverless warehouses, to reduce query re-execution.

Key takeaway

For Analytics Engineers and Data Engineers building or maintaining Databricks AI/BI dashboards, you should prioritize a holistic optimization strategy. Implement page-based designs, configure smart default filters, and leverage parameters to reduce initial data loads. Maximize browser and SQL result caching by ensuring deterministic queries and considering shared execution identities for published dashboards to significantly improve performance and reduce operational costs.

Key insights

Optimizing Databricks AI/BI dashboards requires a holistic approach across design, compute, and data layers.

Principles

• Optimize for "first paint" to improve initial user experience.
• Deterministic queries maximize cache hit rates.
• Smaller, pre-aggregated datasets enhance browser caching.

Method

Optimize dashboard performance by organizing content into pages, setting smart filter defaults, using parameters for data slicing, leveraging browser caching for small datasets, and maximizing Databricks SQL result cache usage through deterministic queries and shared execution identities.

In practice

• Group dashboard visuals into pages/tabs.
• Set default date filters to recent windows.
• Use parameters for large dataset filtering.

Topics

• Databricks AI/BI Dashboards
• Dashboard Performance Optimization
• SQL Warehouse Caching
• Lakehouse Data Patterns
• Query Optimization

Best for: Analytics Engineer, Data Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.