How Power Query is used for Data Transformation

 Power Query is used for data transformation and data preparation. Power Query consists of a graphical user interface for getting data from different sources and a Power Query Editor for filtering the data. Since the engine is available in many products and services, the destination where the data will be stored totally depends on where the Power Query was used. Using the Power Query editor, you can perform the extract, transform, and load (ETL) processing of data.

 

How Power Query helps with Data Acquisition:

Business users consume up to 80 percent of their time on data preparation, which also delays the work of analysis and decision-making.


 

 

Transformations:

The transformation engine in Power Query includes many predefined transformation functions that can be used through the graphical interface of the Power Query Editor. These transformations are often as simple as removing a column or filtering rows, or as common as using the primary row as a table header. There are such advanced transformation options like merge, append, group by, pivot, and unpivot.

All these transformations are made possible with the help of choosing the transformation option in the menu, and then applying the options required for the transformation. The following illustration shows a couple of the transformations available in Power Query Editor:

 


 

Dataflows:


Power Query is used in many products, for instance in Power BI and Excel. However, using Power Query within a product has certain limitations with its usage to only that specific product. Using dataflows, you can get the data and transform it in the same way, but instead of sending the output to Power BI or Excel, you can store the output in other storage options like Dataverse or Azure Data Lake Storage. This way, you'll use the output of dataflows in other products and services.

 

Power Query M Language:

In any data transformation framework, there are some transformations that can't be done in the best way by using the graphical editor. Some of these transformations might require special configurations and settings that the graphical interface does not support currently. The Power Query engine uses a scripting language behind the scenes for all the Power Query transformations: the Power Query M language, also known as M.

The M language is that the data transformation language of Power Query. Anything that happens in the query is ultimately written in M. If you would like to try to advanced transformations using the facility Query engine, you'll use the Advanced Editor to access the script of the query and modify it as you would like. If you discover that the interface functions and transformations won't perform the precise changes you would like, use the Advanced Editor and therefore the M language to fine-tune your functions and transformations.




Power Query experiences:

The Power Query user experience is provided through the facility Query Editor interface . The goal of this interface is to assist you implement the transformations you would like just by interacting with a user-friendly set of ribbons, menus, buttons, and other interactive components.

The Power Query Editor is that the first data preparation experience, where you'll attach with an honest range of data sources and apply many various data transformations by previewing data and selecting transformations from the interface. These data transformation capabilities are common across all the info sources, regardless of the underlying data source limitations.

When you create a replacement transformation step by interacting with the components of the Power Query interface, Power Query automatically creates the M code required to undertake to the transformation so you do not get to write any code.

 

Data analytics is a process of inspecting, cleansing, transforming, and modelling of the data with the aim of discovering useful information, informing conclusions and supporting decision-making. Data analytics has multiple features and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, Data Analytics plays a vital role in making decisions more scientific and helping businesses operate more effectively. Nowadays, the Data analytics masters programming course is the latest and trending language in the corporate world.

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