Data Integration Strategy Advice

Since you're working with a large SQL database, it's crucial to optimize performance while maintaining the ability to transform and analyze your data effectively.

Data Integration Options

1. DirectQuery vs. Import Mode:

   • DirectQuery: Allows real-time data access and queries the database directly, but has limitations on transformations and calculated columns/tables.
   • Import Mode: Imports data into Power BI, enabling full transformation and calculation capabilities but can be slow and resource-intensive for very large datasets.

2. Hybrid Approach:

   • Use DirectQuery for large, frequently changing tables where real-time data access is essential.
   • Use Import Mode for smaller, less frequently changing tables or for pre-aggregated data that requires complex transformations.

Recommended Strategies

1. Optimize SQL Queries:

   • Write efficient SQL queries to reduce the amount of data pulled into Power BI.
   • Use views in your SQL database to pre-aggregate or pre-filter data before it reaches Power BI.

2. Composite Models:

   • Power BI supports composite models, which allow you to use both DirectQuery and Import Mode in the same dataset.
   • Keep large tables in DirectQuery and smaller, more manageable tables in Import Mode.

3. Dataflows:

   • Use Power BI Dataflows to perform ETL (Extract, Transform, Load) operations before loading data into your Power BI dataset.
   • Dataflows store the data in the Power BI service, allowing you to perform transformations and reduce the load on your desktop model.

4. Aggregations:

   • Create aggregated tables to reduce the volume of data queried in DirectQuery mode.
   • Use aggregation tables to handle summarized data and direct detailed queries to the original large tables only when necessary.

5. Partitions and Incremental Refresh:

   • Use partitions to divide large tables into smaller, more manageable chunks.
   • Implement incremental refresh to only refresh data that has changed, reducing the load and time required for data refreshes.

Hi SarahHope ,

Based on what you've described, your SQL data is large and needs to be refreshed daily, based on that your best bet would be to use incremental refreshes. Instead of doing a "full refresh" every day, consider refreshing "incremental partitions". For calculations that need to be done with DAX, you can use import mode for the data.

Incremental refresh for semantic models and real-time data in Power BI - Power BI | Microsoft Learn

Hope it helps!

Best regards,
Community Support Team_ Scott Chang

If this post helps then please consider Accept it as the solution to help the other members find it more quickly.