Power Query is a data transformation tool developed by Microsoft. This is also an automated tool for data transformation. It is used to connect, extract, transform, and load (ETL) data from various sources into tools like Power BI, Excel, SQL and more. It contains features like data transformation, data automation, data connectivity and more. It also helps users to import and clean data for analysis in sources like Excel. It also helps users to prepare data for visualizations and reporting. Along with that there is so much data transformation and data connectivity services which are provided by Power BI.
What is Power Query used for?
Power query is mainly used for Transforming, loading (ETL) and Extracting data in tools like Power BI and Excel. With the help of Power query, users can prepare data automatically without any kind of coding skills.
Along with these things there are so many common uses of power query mentioned below:
Common Uses of Power Query | Description |
Data Import & Connectivity | It Connects to multiple sources like Excel, databases, APIs, cloud services, web pages, and many more. It also combines data from different files or databases. |
Data Cleaning & Transformation | It Removes duplicates, blanks, and errors. It also Splits, merges, and reshapes columns. It also Changes data types like text to numbers, dates, etc. |
Data Automation & Refreshing | It Automates repetitive data cleaning and processing tasks. It also Updates data automatically when the source changes. |
Merging & Combining Data | It also Joins multiple tables from different sources. It attaches data (stacking tables) or merges datasets (like SQL joins). |
Creating Custom Columns & Formulas | It Adds calculated columns using M language or built-in transformations. It also Applies conditional logic for data classification. |
Pivoting & Unpivoting Data | It Reshapes data for better analysis and reporting. It also Converts wide tables into long format (and vice versa). |
Loading Data into Excel or Power BI | It also loads the transformed data into Power BI for dashboards and reports. It Loads the cleaned data into Excel for further analysis with formulas and pivot tables. |
Is Power Query the same as Excel?
There is so much confusion people are having as its power query is same as Excel, so, no, Power Query is not the same as Excel, because it is a tool within Excel which is used to helps you with transfer the data and do some kind of automation as well.
Main Differences Between Power Query and Excel:
Feature | Power Query | Excel |
Purpose | Data extraction, transformation, and automation. | Data analysis, calculations, visualization, and reporting. |
Main Function | Cleans, reshapes, and loads data into Excel. | Uses formulas, PivotTables, and charts for analysis. |
User Interface | Query Editor (separate window from Excel). | Worksheet-based (cells, tables, formulas). |
Automation | Automatically refreshes queries when data updates. | Requires VBA, Macros, or Power Automate for automation. |
Data Handling | Works well with large datasets (millions of rows). | Limited by worksheet size (1,048,576 rows per sheet). |
Language | Uses M Language for transformations. | Uses Excel formulas and sometimes VBA for advanced tasks. |
Power Query M formula language?
Power Query M language is a functional programming language which is used to transform, extract and load (ETL) data from various sources in Power Query. It is primality optimized for data manipulation.
Now let's find out some features of M Language:
Functional Language
Uses functions instead of step-by-step instructions.
Case-Sensitive
Table.SelectRows is different from table.selectrows.
Immutable Data
Once a variable is assigned, it cannot be changed.
Query Steps & Expressions
Each transformation step builds on the previous one.
Power Query Interface Generates M Code
You can edit or write custom M code in the Advanced Editor.
Who uses Power Query?
Business professionals and data analysts and data providers use Power Query across various industries to transform, extract and load (ETL) data for more efficiency. Let’s see who exactly using this for which purpose below:
User Type | Usage |
Business Analysts | Preparing data for reports and dashboards. |
Data Analysts & Scientists | Data wrangling before advanced analysis. |
Finance & Accounting Teams | Automating financial reports. |
IT & Data Engineers | Pre-processing data before loading it into databases. |
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