Question

Your Power BI dataset has millions of rows and is causing memory issues. How can you reduce the dataset size without losing key insights?
You are working with a Power BI dataset containing millions of rows, leading to high memory consumption and performance issues. What strategies can be used to optimize the dataset size while maintaining key business insights? Consider techniques such as data compression, aggregation, or filtering unnecessary data.

Answer 1

Here are a few cutting-edge optimization techniques that will help keep all critical insights while reducing the dataset size in Power BI:

1. Data Reduction Techniques

Remove unnecessary Columns: Though the report may contain raw transaction data, the aggregated values can be retained; thereby, only relevant fields are kept instead of unnecessary fields.

Filter Data Before Loading: Use SQL queries or Power Query to filter out unwanted previous versions and dated low-value data.

Reduce Granularity: Rather than storing every transaction, pre-aggregate data daily, weekly, or monthly to the extent possible.

2. Data Compression & Optimization

Optimize Data Types: To compress better, transform high-cardinality textual fields into numeric values or categorical IDs.

Disable Auto Date/Time: This feature generates unnecessary hidden tables; having a dedicated date table is much more efficient.

Star Schema: Normalize large tables into fact and dimension tables so that they do not store redundant data.

3. Performance Tuning

Use Aggregations: Define summary tables that hold frequently accessed data to lessen the amount of hits on the big tables.

Incremental Refresh: Instead of refreshing the entire dataset, refresh only the new/changed data.

Consider Composite Models, where historical data are maintained in Import Mode for optimization, and DirectQuery is for live access should it be needed.