Data Transformations with ADF: Power Query
Explore how Power Query enables effective data transformation and cleaning within Azure Data Factory. Learn the steps to build data pipelines that perform operations like filtering, replacing, and merging across various data sources. This lesson equips you to create and run Power Query pipelines that streamline data processing and ensure consistent, integrated datasets for analysis.
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Data processing and manipulation is a crucial part of modern businesses. As companies deal with increasingly larger datasets, data processing becomes more complex and requires efficient tools. One such tool is Power Query, a data transformation and cleaning tool available in ADF. This lesson will discuss how Power Query can be used as a data pipeline in Azure Data Factory and how it helps in data manipulation and processing.
Power Query in Azure Data Factory
Power Query is a powerful data transformation and cleaning tool provided by Microsoft. It is available as an add-in for Excel and can be used with various other Microsoft products, including Azure Data Factory. Power Query enables users to perform various data transformations, such as filtering, merging, and pivoting. It provides a user-friendly interface that allows users to create modifications using a drag-and-drop approach.
Importance of Power Query in data manipulation and processing
Data cleaning and transformation are essential steps in data processing. Raw data often contains errors, inconsistencies, and missing values, making it difficult to analyze and draw meaningful insights. Power Query helps to overcome these challenges by providing a range of data transformation capabilities. Some of the benefits of using Power Query in data manipulation and processing include:
Microsoft Learn documentation provides details on how power queries are a derived form of calculations done on ...