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

Summary

This article serves as a comprehensive guide for optimizing the performance of AI/BI dashboards within the Databricks environment. It outlines ten best practices aimed at enhancing dashboard responsiveness and scalability, particularly as user interactions increase. The author emphasizes a holistic approach that encompasses dashboard design, SQL warehouse configuration, and data management in Lakehouse architectures. Key strategies include organizing dashboards into pages to manage query loads, setting smart default filters to improve first paint times, utilizing parameters for efficient data slicing, leveraging browser caching for small datasets, and maximizing result cache usage to minimize redundant queries. Each optimization technique is supported by a rationale that connects user experience with backend performance, ensuring that developers can make informed decisions based on their specific use cases.

Key Learnings

• 1Organizing dashboards into multi-page formats can significantly reduce query execution bursts and improve user experience.
• 2Setting smart defaults for filters optimizes the initial loading experience, enhancing perceived performance during peak usage.
• 3Using parameters effectively can minimize data processing load by filtering datasets before executing SQL queries.
• 4Browser caching can drastically improve interaction speeds for small datasets, reducing backend load and latency.
• 5Maximizing the use of result caching can lead to substantial performance gains by serving cached results instead of re-executing queries.

Who Should Read This

Senior Data Engineers and BI Developers focused on optimizing performance and scalability of Databricks AI/BI dashboards.

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