Get, clean, transform, and load data from various sources using Power Query.
Covers connecting to data sources, configuring data source settings, profiling and cleaning data, resolving import errors, transforming data using Power Query, and preparing data for loading into the data model.
5 minutes
5 Questions
Preparing data in Power BI is a critical phase that transforms raw information into a clean, structured format suitable for analysis and visualization. This process occurs primarily within Power Query Editor, where analysts shape and refine their datasets before loading them into the data model.
The preparation phase begins with connecting to various data sources such as Excel files, SQL databases, cloud services, or web APIs. Once connected, analysts evaluate data quality by examining column distributions, identifying errors, and detecting anomalies that could impact analysis accuracy.
Key data preparation tasks include removing unnecessary columns and rows that add no analytical value, filtering data to focus on relevant subsets, and handling null or missing values appropriately. Analysts often need to change data types to ensure columns are formatted correctly as text, numbers, dates, or other appropriate types.
Transformation operations form the core of data preparation. These include splitting columns based on delimiters, merging columns to create new fields, pivoting and unpivoting data to reshape tables, and creating calculated columns using custom formulas. Analysts frequently rename columns for clarity and consistency across the data model.
Combining data from multiple sources is another essential aspect. Power Query enables merging queries similar to SQL joins and appending queries to stack tables with matching structures. This consolidation creates comprehensive datasets from disparate sources.
Data profiling tools help analysts understand their data better by showing value distributions, identifying unique values, and highlighting potential quality issues. Error handling ensures problematic rows are addressed through replacement, removal, or correction.
Applied steps in Power Query create a repeatable transformation recipe, meaning when source data updates, all preparation steps execute automatically. This automation ensures consistency and saves significant time during data refresh cycles.
Proper data preparation establishes a solid foundation for building accurate reports and meaningful insights that drive business decisions.Preparing data in Power BI is a critical phase that transforms raw information into a clean, structured format suitable for analysis and visualization. This process occurs primarily within Power Query Editor, where analysts shape and refine their datasets before loading them into the data model.
T…