Summary  
This chapter covers data cleaning and transformation operations in a query-based data pipeline, including filtering and deduplicating rows, correcting errors, changing data types, renaming and splitting or merging columns, unpivoting/pivoting data, aggregating values, and profiling columns through recorded transformation steps.

General domain of usage  
Data preparation for business intelligence and reporting

In Power Query, **data cleaning** and **transformation** involve several key steps to prepare raw data for analysis. Initially, you import data from various sources such as Excel, databases, or web pages. The **cleaning** process includes removing duplicates, filtering rows, and correcting errors. **Transformation** steps involve renaming columns, changing data types, and merging or splitting columns to structure the data properly. 

**Advanced transformations** include unpivoting columns, creating calculated columns, and aggregating data. These actions help ensure the data is accurate, consistent, and ready for creating insightful visualizations and reports in Power BI.


What is the first step in preparing your data for cleaning in Power BI?

Data Cleaning and Transformation