How do I design a scalable Data Warehouse architecture for real-time BI reporting?

All answers to this question.

The key is the "Transformation" layer. If your data isn't cleaned before it hits the BI tool, the tool will always be slow regardless of the warehouse.

   Answered 2024-06-11 by Jessica Brown

• Correct, Jessica. We found that moving our heavy calculations from Power BI measures back into the SQL layer improved our dashboard refresh times by 60%.

     Commented 2024-06-12 by Christopher Evans

Is the "Lakehouse" architecture like Databricks really necessary for a mid-sized company, or is a standard Data Warehouse enough?

   Answered 2024-06-12 by David Clark

• David, for most mid-sized firms, a Lakehouse might be overkill unless you're doing heavy Machine Learning alongside your BI. A well-structured Snowflake or BigQuery instance (Standard Warehouse) will handle 95% of your reporting needs with less complexity and lower management costs.

     Commented 2024-06-14 by Thomas Wright

Modern BI has shifted almost entirely to ELT (Extract, Load, Transform). In a 2024 project, we moved to an architecture where we dumped raw data into a Data Lake (S3) and used dbt (Data Build Tool) to transform it within Snowflake. This leverages the cloud's massive compute power. By separating your compute from your storage, you can run heavy BI queries without affecting your production environment's performance. It also allows your Business Analysts to write SQL transformations directly, reducing the bottleneck on the IT department.

   Answered 2024-11-14 by Melissa Robinson