![]() ![]() Power Query can be used in many products, such as Power BI and Excel. More information: Quickstart: Using Power Query in Power BI Dataflows The following illustration shows a few of the transformations available in Power Query Editor. There are also advanced transformation options such as merge, append, group by, pivot, and unpivot.Īll these transformations are made possible by choosing the transformation option in the menu, and then applying the options required for that transformation. These transformations can be as simple as removing a column or filtering rows, or as common as using the first row as a table header. The transformation engine in Power Query includes many prebuilt transformation functions that can be used through the graphical interface of the Power Query Editor. Power Query for Desktop-Found in integrations such as Power Query for Excel and Power BI Desktop.Īlthough two Power Query experiences exist, they both provide almost the same user experience in every scenario.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.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.Ĭurrently, two Power Query experiences are available: These data transformation capabilities are common across all data sources, whatever the underlying data source limitations. 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. 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 user experience is provided through the Power Query Editor user interface. Because Power Query provides connectivity to hundreds of data sources and over 350 different types of data transformations for each of these sources, you can work with data from any source and in any shape. Power Query queries can be refreshed manually or by taking advantage of scheduled refresh capabilities in specific products (such as Power BI) or even programmatically (by using the Excel object model). Power Query offers the ability to work against a subset of the entire data set to define the required data transformations, allowing you to easily filter down and transform your data to a manageable size. Volume (data sizes), velocity (rate of change), and variety (breadth of data sources and data shapes) In the event that you need to modify the process or query to account for underlying data or schema changes, you can use the same interactive and intuitive experience you used when you initially defined the query. When using Power Query to access and transform data, you define a repeatable process (query) that can be easily refreshed in the future to get up-to-date data. Highly interactive and intuitive experience for rapidly and iteratively building queries over any data source, of any size.Īny shaping is one-off and not repeatable ![]() ![]() Power Query enables connectivity to a wide range of data sources, including data of all sizes and shapes.Įxperiences for data connectivity are too fragmentedĬonsistency of experience, and parity of query capabilities over all data sources.ĭata often needs to be reshaped before consumption Existing challengeįinding and connecting to data is too difficult Several challenges contribute to this situation, and Power Query helps address many of them. How Power Query helps with data acquisitionīusiness users spend up to 80 percent of their time on data preparation, which delays the work of analysis and decision-making. Diagram with symbolized data sources on the left, passing through Power Query for transformation in the center, and then going to four destinations on the right: Microsoft Azure Data Lake Storage, Microsoft Dataverse, Microsoft Excel and Microsoft Power BI. ![]()
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