List the most common techniques for data shaping in Power BI?Power BI interview questions and answers 269

 List the most common techniques for data shaping in Power BI?

Here are some of the most common techniques for data shaping in Power BI, primarily performed within the Power Query Editor:

1. Renaming Columns and Tables:

• Assign meaningful names to columns and tables for clarity and better understanding.

• Use clear and consistent naming conventions to improve readability and maintainability.

2. Removing Columns:

• Eliminate unnecessary columns that don't provide relevant information for analysis.

• Reduce the size of the dataset and make it easier to manage.

3. Changing Data Types:

• Ensure columns have the correct data types (e.g., text, numbers, dates, etc.) to enable accurate analysis and calculations.

• Fix any inconsistencies in data types.

4. Filtering Rows:

• Select specific rows based on criteria to focus on relevant data.

• Exclude irrelevant or unwanted rows.

5. Sorting Rows:

• Order rows based on values in one or more columns to organize the data for better visual analysis.

6. Removing Duplicates:

• Eliminate rows with identical values in all columns to avoid redundancy and potential errors in calculations.

7. Grouping Rows:

• Aggregate data based on common values in a column to summarize and analyze it at a higher level.

• Use operations like sum, count, average, etc., to create summary statistics.

8. Pivoting Columns:

• Reshape the data by rotating columns into rows to create a different view of the information.

• Useful for analyzing relationships between multiple dimensions.

9. Creating Custom Columns:

• Add new columns based on calculations, transformations, or extractions from existing columns.

• Enhance the dataset with additional insights or calculations.

10. Splitting Columns:

• Divide a single column into multiple columns based on delimiters or patterns.

• Break down complex data structures into more manageable parts.

11. Replacing or Filling Values:

• Modify or fill missing values with specific data to address inconsistencies or gaps in the data.

• Ensure data completeness and accuracy.

12. Merging Queries:

• Combine data from multiple sources (e.g., tables, files) into a single query for comprehensive analysis.

• Create unified datasets for multidimensional analysis.

13. Appending Queries:

• Add rows from one query to another to create a larger dataset.

• Expand the scope of the data.

Remember: These techniques are often used in combination to achieve the desired data structure and prepare it for effective analysis and visualization in Power BI.