some key strategies to optimize the data model
Limit Data Volume: Import only the necessary data into Power BI. Filter the data at the data source level to reduce the volume of data brought into the model.
Data Type Optimization: Use appropriate data types for your columns to minimize memory consumption and improve query performance.
Data Model Relationships: Set up relationships between tables using primary and foreign keys. Establishing proper relationships improves query performance and enables more intuitive report creation.
Avoid Many-to-Many Relationships: Many-to-many relationships can negatively impact performance. Use bridge tables or transform data to handle many-to-many relationships.
Use DAX Best Practices: Write efficient DAX expressions and measures. Avoid using heavy calculations in calculated columns when possible.
Use DirectQuery Mode: For large datasets, consider using DirectQuery mode to query the data source directly instead of importing data into the data model.
Data Aggregation: Pre-aggregate data at the data source level whenever possible. Use summarization tables to store aggregated values for faster query performance.
Data Partitioning: For large datasets, consider partitioning your data into smaller subsets to improve query response time.
Data Compression: Power BI automatically compresses data, but you can optimize the compression by using the "Auto" and "None" data load options where appropriate.
Manage Relationships and Cross Filters: Configure relationships and cross filters appropriately to avoid unnecessary data filtering.
Use Query Diagnostics: Utilize the Query Diagnostics feature in Power BI to identify performance bottlenecks in your data model.
Data Refresh Schedule: Set an appropriate data refresh schedule that balances data freshness with system performance.
Remove Unnecessary Columns: Eliminate unnecessary columns from your data model to reduce memory consumption.
Enable Load Settings: Use load settings like "Load" or "Load and enable load" instead of "Load and enable load on demand" to improve report rendering time.
Use Paginated Reports for Large Datasets: Consider using paginated reports for large datasets with hundreds of thousands of rows to optimize performance.
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