DATA REDUCTION IN POWER BI

In Power BI, data reduction refers to the process of reducing the amount of data that needs to be loaded into memory and processed for analysis. This is typically done to improve report performance by reducing the time it takes to load and refresh the report.

Data reduction techniques include filtering, aggregating, summarizing, partitioning, indexing, query folding, data modeling, compression, data cleansing, and the use of DirectQuery and Live Connection. These techniques allow users to focus on relevant data and remove unnecessary information, resulting in faster report performance and more accurate analysis.

Here are the topics related to data reduction in Power BI:

1. Filtering
2. Aggregating
3. Summarizing
4. Partitioning
5. Indexing
6. Query folding
7. Data modeling
8. Compression
9. Data cleansing
10. Use of DirectQuery and Live Connection

More detailed explanation of each of the topics related to data reduction in Power BI:

1) Filtering: Filtering is a technique that involves applying filters to limit the amount of data displayed in a report. This can be done using various filter types, including slicers, visual-level filters, and page-level filters. Filtering helps users to focus on specific data and reduce the amount of irrelevant information, which can improve the clarity and accuracy of the report.

2) Aggregating: Aggregating is a technique that involves grouping data by a specific column and then applying an aggregate function, such as sum, count, or average, to the data in each group. This reduces the number of rows and columns in the dataset, making it easier to visualize and analyze the data. Aggregating can be done using the Group By and Aggregate functions in Power Query or the DAX language.

3) Summarizing: Summarizing is similar to aggregating, but it involves creating summary tables or charts that display key metrics, such as sales revenue or customer count. This technique allows users to quickly identify trends and insights without having to sift through large amounts of data. Summarizing can be done using various visualizations in Power BI, such as tables, matrices, and charts.

4) Partitioning: Partitioning is a technique that involves splitting a large dataset into smaller, more manageable parts. This can be done using various partitioning strategies, including time-based partitioning, geographic partitioning, and value-based partitioning. Partitioning helps to improve report performance by reducing the amount of data that needs to be loaded into memory and processed.

5) Indexing: Indexing is a technique that involves creating indexes on columns that are frequently searched or sorted. This helps to improve the performance of queries, as the indexes allow the data to be retrieved more quickly. Indexing can be done using the Power Query Editor or by creating indexes in the data source.

6) Query folding: Query folding is a technique that allows the Power Query engine to push queries back to the data source to reduce the amount of data brought into Power BI. This improves report performance, as less data needs to be transferred between the data source and Power BI. Query folding can be enabled using the Options dialog in Power Query.

7) Data modeling: Data modeling is a technique that involves creating relationships and hierarchies in the data model to improve report performance. This helps to reduce the amount of data needed to be loaded into memory, resulting in faster report performance. Data modeling can be done using the Relationship view in Power BI Desktop.

8) Compression: Compression is a technique that involves reducing the size of the data model by compressing data and removing unnecessary columns. This helps to improve report performance by reducing the amount of data that needs to be loaded into memory. Compression can be done using the Options dialog in Power Query or by removing unused columns in the data model.

9) Data cleansing: Data cleansing is a technique that involves cleaning and transforming data to remove errors, inconsistencies, and irrelevant information. This helps to ensure that the data is accurate and reliable for analysis. Data cleansing can be done using various tools in Power Query, such as the Replace Errors and Transform Data functions.

10) Use of DirectQuery and Live Connection: DirectQuery and Live Connection are techniques that allow users to query data directly from the data source instead of importing it into Power BI. This helps to reduce the amount of data loaded into memory, resulting in faster report performance. DirectQuery and Live Connection can be configured using the Connection dialog in Power BI Desktop.