Enhancing Performance with Aggregated Tables To enhance Power BI performance, creating an aggregated table is essential. This practice helps manage large datasets by summarizing data into a more manageable format for visualizations. The aggregation process involves grouping records and applying necessary calculations to reduce the load on queries from massive fact tables.
Methods for Creating Aggregated Tables Three methods exist for creating aggregated tables: using Power Query, DAX calculated tables, or SQL commands in relational databases like Lakehouse. Each method has its advantages; Power Query allows ETL separation while DAX operates within the semantic model of Power BI itself. SQL provides flexibility when working directly with database structures but may limit reusability compared to other approaches.
Connecting Aggregated Tables Effectively After establishing an aggregated table, it’s crucial to connect it properly with dimension tables such as date and product categories. This connection can lead to specific storage modes that optimize query performance further—especially important in composite models combining direct query and imported data sources.