large data volume

[powerbiexpert22]

how do i perform development by importing tables that has larger data volume (millions of records in fact table and dimension tables having around 100K records)?

[Ritaf1983 · Accepted solution]

Hi @powerbiexpert22 

When developing in Power BI with large datasets, including fact tables with millions of records and dimension tables with around 100K records, it's crucial to manage performance and resource usage efficiently. Here are several strategies you can employ:

1. Use DirectQuery Mode

• DirectQuery allows you to work with large datasets without importing the data into Power BI. Instead, queries are sent directly to the data source whenever the report is used.
• This mode can be useful for development if you want to avoid loading large amounts of data into memory. However, be mindful that it can lead to performance issues if your data source or queries are slow.

2. Use Import Mode with Incremental Refresh

• Import mode is generally faster than DirectQuery for report performance, but it loads the entire dataset into memory.
• Incremental Refresh can help manage large datasets by only loading and refreshing parts of the data that have changed or are new, reducing the volume of data that needs to be processed during refreshes.

3. Development with a Sample Dataset

• Consider creating a sample dataset with a representative subset of your data. This allows for faster development and testing.
• Once the report is built and optimized with the sample data, you can switch to the full dataset for final testing and deployment.

4. Aggregations

• Aggregation tables can significantly improve performance by summarizing data at a higher level and reducing the volume of data that needs to be processed for queries.
• Use aggregation tables in conjunction with DirectQuery for detailed data exploration as needed.

5. Partitioning

• If using a data warehouse or database that supports it, consider partitioning your large tables. This can improve query performance and make your data loads more efficient.
• Power BI can handle partitions well, especially when combined with Incremental Refresh.

6. Optimize DAX Calculations and Model Design

• Ensure your data model is optimized, with well-designed relationships and DAX calculations.
• Avoid using complex measures that require scanning millions of records whenever possible; instead, pre-aggregate data if feasible.

7. Manage Data Loading (Query Folding)

• Use Query Folding in Power Query to push as much processing as possible back to the data source. This reduces the load on Power BI and improves performance.
• Ensure that your transformations in Power Query are compatible with query folding to optimize performance.
  

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

[Brunner_BI · Accepted solution]

I would add to remove all unused columns to optimize your model.

[Ritaf1983 · Accepted solution]

Hi @powerbiexpert22 

When developing in Power BI with large datasets, including fact tables with millions of records and dimension tables with around 100K records, it's crucial to manage performance and resource usage efficiently. Here are several strategies you can employ:

1. Use DirectQuery Mode

• DirectQuery allows you to work with large datasets without importing the data into Power BI. Instead, queries are sent directly to the data source whenever the report is used.
• This mode can be useful for development if you want to avoid loading large amounts of data into memory. However, be mindful that it can lead to performance issues if your data source or queries are slow.

2. Use Import Mode with Incremental Refresh

• Import mode is generally faster than DirectQuery for report performance, but it loads the entire dataset into memory.
• Incremental Refresh can help manage large datasets by only loading and refreshing parts of the data that have changed or are new, reducing the volume of data that needs to be processed during refreshes.

3. Development with a Sample Dataset

• Consider creating a sample dataset with a representative subset of your data. This allows for faster development and testing.
• Once the report is built and optimized with the sample data, you can switch to the full dataset for final testing and deployment.

4. Aggregations

• Aggregation tables can significantly improve performance by summarizing data at a higher level and reducing the volume of data that needs to be processed for queries.
• Use aggregation tables in conjunction with DirectQuery for detailed data exploration as needed.

5. Partitioning

• If using a data warehouse or database that supports it, consider partitioning your large tables. This can improve query performance and make your data loads more efficient.
• Power BI can handle partitions well, especially when combined with Incremental Refresh.

6. Optimize DAX Calculations and Model Design

• Ensure your data model is optimized, with well-designed relationships and DAX calculations.
• Avoid using complex measures that require scanning millions of records whenever possible; instead, pre-aggregate data if feasible.

7. Manage Data Loading (Query Folding)

• Use Query Folding in Power Query to push as much processing as possible back to the data source. This reduces the load on Power BI and improves performance.
• Ensure that your transformations in Power Query are compatible with query folding to optimize performance.
  

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