_edited_edited.png)
Power Query is a data transformation and data preparation engine. Power Query comes with a graphical
interface for getting data from sources and a Power Query Editor for applying transformations. Because the
engine is available in many products and services, the destination where the data will be stored depends on
where Power Query was used. Using Power Query, you can perform the extract, transform, and load (ETL)
processing of data.
How and When Can You Use It?
With Power Query, users can connect to various data sources, such as spreadsheets, databases, and cloud-based services, and perform data transformation tasks. These tasks include removing duplicates, filtering and sorting data, merging and appending data, and splitting columns.
Power Query experiences
The Power Query user experience is provided through the Power Query Editor user interface. The goal of this interface is to help you apply the transformations you need simply by interacting with a user-friendly set of ribbons, menus, buttons, and other interactive components.
The Power Query Editor is the primary data preparation experience, where you can connect to a wide range of data sources and apply hundreds of different data transformations by previewing data and selecting transformations from the UI. These data transformation capabilities are common across all data sources, whatever the underlying data source limitations.
When you create a new transformation step by interacting with the components of the Power Query interface, Power Query automatically creates the M code required to do the transformation so you don't need to write any code.
Currently, two Power Query experiences are available:
Power Query Online—Found in integrations such as Power BI dataflows, Microsoft Power Platform dataflows, Azure Data Factory wrangling dataflows, and many more that provide the experience through an online webpage.
Power Query for Desktop—Found in integrations such as Power Query for Excel and Power BI Desktop.
Key Differentiators of Power Query
Power Query is a powerful tool that sets itself apart in several ways:
It enables seamless Integration: Power Query is closely integrated with other Microsoft tools, such as Excel and Power BI. This means that users can leverage existing data and workflows, making them easier to adopt and use.
It gives you advanced data transformation capabilities: Power Query provides advanced data transformation capabilities, including the ability to clean and transform data using a wide range of built-in transformations. Users can also create custom functions using the M language, enabling them to perform complex ETL tasks on large datasets.
It enables you to perform automated data cleansing: Power Query includes automated data cleansing capabilities, such as removing duplicates, identifying, and correcting errors, and filling in missing data. This helps to ensure that the data is clean and consistent, reducing the risk of errors and improving the accuracy of the analysis.
Pros and Cons
As is the case with most solutions, Power Query also has some pros and cons:
Pros:
User-friendly interface and intuitive drag-and-drop features
Seamless integration with other Microsoft tools, such as Excel and Power BI
Wide range of data sources supported, including cloud-based services and file formats
Advanced data transformation capabilities, including custom functions and M language support
Automated data cleansing and error detection features
Cons:
Steep learning curve for advanced features and custom functions
Limited customization options for some data transformations and visualizations
Can be resource-intensive and slow for extensive datasets
Not available natively in all Microsoft Office applications, requiring separate installation for some tools
Limited support for certain data sources and file formats
Having all of these words in a sentence can be confusing sometimes — but don’t fret, we’re here to clarify that!
Transpose, Pivot and Unpivot are amazing transformation techniques used in data analysis and reporting. They help make the analysis easier and simpler to follow through.
Now, we would look at each of these techniques — one after the other:
Transpose
In the simplest form — to transpose means to flip a table such that the rows become columns and columns become rows. This is helpful when you want to rearrange the way data is displayed or if you need to perform calculations on data that isn’t organized the way you need it.
For example, suppose we have the following table of data:
If we transpose this table, the rows become columns and the columns become rows:
Notice that, the rows and columns have been reversed.
Pivot
In general, to pivot means to turn or rotate around a central point. In Power Query, “Pivot” is a transformation step that allows you to transform a table by rotating its columns into rows or rows into columns, and aggregating the data based on the values in those columns.
when we do Pivot the city column , The rows of city column transform into each city into columns.
The resulting pivot table will look like this:
Pivot and transpose are both ways to rotate tables, but they do it in different ways. Pivot turns the table sideways and combines data, while transpose just flips the table sideways without changing the data.
Unpivot
Unpivoting is the process of transforming column-based data into row-based data. This is the opposite of the Pivot transformation we discussed earlier.
After Unpivoting, the resulting table should look like this:
As you can see, the Unpivot transformation changed the data’s format from one that was based on columns to one that was based on rows.
Conclusion
Try it out for yourself: If you’re new to transpose, pivot, and unpivot in Power Query, try using these tools with your own data to see how they work. You might be surprised at how much easier it is to analyze your data once you know how to use these tools effectively.