The Top 10 Best Practices for AI/BI Dashboards Performance Optimization (Part 2)

Summary

This article serves as a comprehensive guide for optimizing AI/BI dashboards in Databricks, focusing on performance improvements as usage scales. It outlines ten best practices that encompass dashboard design, warehouse configuration, and data modeling strategies. Key optimizations discussed include selecting appropriate warehouse types, applying star schema data modeling, leveraging Parquet optimization techniques, and utilizing materialized views for efficient data retrieval. The article emphasizes the importance of minimizing data scanned per query, maintaining clean and efficient data types, and ensuring that the system can handle peak loads without performance degradation. By following these practices, teams can achieve faster, more stable dashboard performance, ultimately enhancing user experience and operational efficiency.

Key Learnings

• 1Optimize warehouse configurations to match dashboard usage patterns, ensuring low-latency responses during peak loads.
• 2Implement star schema data modeling to reduce join complexity and improve query performance.
• 3Utilize Parquet optimization techniques to minimize data read per query, enhancing overall dashboard responsiveness.
• 4Leverage materialized views to precompute frequently accessed aggregates, significantly reducing the amount of data scanned during user interactions.
• 5Choose appropriate data types to improve cache efficiency and reduce IO costs, thereby enhancing dashboard performance.

Who Should Read This

Senior Data Engineers and BI Analysts seeking to enhance the performance and scalability of AI/BI dashboards in Databricks.

Test Your Knowledge

Topics