Power Query is a data transformation and analysis tool that helps you extract and transform data from different sources. It’s built into Excel so that you can create a wide range of reports and visualizations.

If you need to get more familiar with Power Query, don’t worry! This article will explain what it is and how it works.

What is Power Query?

Power Query is a Microsoft Power BI data analysis and preparation tool. It provides the ability to import, transform and combine data from many different sources, including files or directories, databases, web pages, and spreadsheets. The transformations that can be applied to the data include filtering by value, column, or key; calculations based on columns of information; combining multiple tables into one table; rearranging rows and columns within a table; comparing two tables’ rows using common column values as criteria for comparison.

Power Query helps prepare data for analysis in Power BI by importing it from multiple sources into a single location where it can be combined with other datasets or transformed before loading into a report such as Excel worksheets or dashboards.

What is Power Query in Microsoft Power BI?

How Power Query helps with data acquisition

Power Query uses the Open Data Protocol (OData) standard to connect to web-based data services such as Facebook, Google Analytics, and the World Bank.

You can import data from multiple sources, including files, web sources, cloud sources, and databases. You can also import data from flat files or text files.

In addition to this flexibility in how you define your connections, Power Query allows users to compare different datasets visually by using a range bar graph or heat map visualization. This feature helps users quickly identify patterns or outliers in their dataset without knowing what those patterns might be like.

Power Query also comes with various data transformations and query functions that users can apply to their datasets. These include measures such as average, count, and sum. The software can also perform various types of statistical analysis on your data sets, including linear and logistic regression.

Power Query experiences

On the other hand, if you want a tool to transform data from sources like CSV and excel files into a set of tables that can be imported into tabular databases like Power Pivot, then Power Query is what you need.

Power Query is part of the Power BI suite. Although it was previously only available as an add-on for Excel 2013 or 2016, since its launch on April 1st, 2018, as part of Microsoft’s new Power BI service, it has become free for all users.

The main purpose of Power Query within the suite is to enable users to import data from various sources (CSV files in this case) and perform operations such as filtering and grouping using visualizations such as tables and charts. After these transformations have been made, they will show up in your dashboard, which can be used by other queries or reports requiring them as inputs.

Power Query is an Excel add-in, which means you can’t use it outside the Microsoft Office suite. However, there is an open-source alternative called OpenRefine, which has a free web version.

The Power BI service is a cloud-based data analytics platform that offers users a variety of tools for working with data. The Power Query add-in for Excel is one of those tools, but it is also available as part of the Power BI service.


Transformations are a way to manipulate data and add or remove columns and rows from your data source. This is done in the Query Editor and Data Modeling view. You can also apply transformations in the Visualization Editor when building visualizations (more on this later).

You’ll use transformations such as:

  • Remove Duplicates: Remove duplicate rows from your data set.
  • Add Columns: Adds new columns to your existing dataset based on existing values or patterns in the table schema.
  • Rename Columns: Rename existing column names with new names that you choose.
  • Union: combines data from two or more tables into a single table that contains all the rows and columns from each source table. You can then join this new table with another table to form a new dataset for analysis.
  • Rows: adds new rows to your dataset based on existing values or patterns in the table schema.


The second way to import data into Power BI is through the Dataflows feature. Dataflows are like an automated ETL process where you can combine multiple data sources in a single step.

Power Query supports many different data sources and can be used in both Power BI Desktop and Power BI Service. In this article, you’ll learn how to use the Refreshable Web Query (RWW) connector with Power Query to bring external data into your reports from any location on the web, including relational databases, OData feeds, and third-party APIs.

First, open a new query to import data using the RWW connector in Power Query. Then, select the “+” button on the top left and choose “From Other Sources > From Web.” In the window that appears, paste your RWW URL into the “URL” field and hit enter.

Power Query M formula language

Power Query M is a formula language used to create transformations, dataflows, and Power Query experiences. It’s based on the Excel M formula language, which you can read more about here.

Power Query M is a subset of the Excel M formula language. You can use a subset of the available functions within Power Query M to create transformations, dataflows, and Power Query experiences.

Power Query M supports the following functions:

LOOKUP: Find a value in a table and return the corresponding result. 

VLOOKUP: Find a value in a table and return the corresponding result. This function is similar to LOOKUP but can be used with vertical or horizontal tables.

INDEX: Get the value of an element in a range based on its position. The syntax is INDEX(range, row_num, [column_num]). This function can be used with vertical or horizontal tables.

MATCH: Find the relative position of a data item within another data set. The syntax is MATCH(lookup_value, lookup_array), where lookup_value is the value you want to match, and lookup_array is the array of values you’re searching through.

OFFSET: Get the value of an element in a range based on its position relative to another cell. The syntax is OFFSET(range, row_num, [column_num]). This function can be used with vertical or horizontal tables.

ROW: Return the row number of a cell. The syntax is ROW([reference]). This function can be used with vertical or horizontal tables. 

COL: Return the column number of a cell. The syntax is COL([reference]). This function can be used with vertical or horizontal tables.

Where can you use Power Query?

Power Query is a data transformation tool as well as a query builder. You can import, transform and combine massive amounts of data from various sources such as Excel, Access, and SharePoint Online.

Power Query is available in the cloud and on-premises (with the Microsoft Data Platform), so it’s compatible with Excel Online and Excel 365 plans that include Power BI Pro or Power BI Premium subscriptions. It also works with most applications using the Microsoft SQL Server database engine for cloud services such as Azure DMS, Azure HDInsight, or Microsoft SQL Server 2019 LocalDB Express Edition (LE).

If you’re looking for an alternative way to connect to your data without having to learn SQL syntax, then I would recommend checking out Power Query because this tool makes it easy for anyone who wants to learn how to build these types of queries without needing any knowledge about databases at all!


Power Query is a powerful tool that can help you to import data into your Power BI reports. It can also transform and clean your data before importing it into Power BI or creating new fields in the imported dataset.

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