What is Power Query? Then We Can Think of Applying the Same in Power BI

Data is everything in the world of business intelligence, which is why raw data often does not come neatly packaged for ready analysis. How Power Query comes to rescue? It is one of the most powerful entities in Power BI ecosystem by which users can change row data into landed insights. But what is Power Query, and why do you need it so much in Power BI?
What is Power Query, what does it do and why every power BI user should learn how to use it
What is Power Query Why It Matters in Power BI

What is Power Query?

Power Query is a data transformation and preparation engine developed by Microsoft. Its functionality is used in Microsoft products like Excel, Power BI, Analysis Services, etc. In Power BI, Power Query helps in connecting to different data sources and do the heavy lifting of cleaning, reshaping and transforming raw dirty data from multiple transactions before using it with the visualization layer in building dashboards and reports.
Power Query is also like the ETL tool (Extract, Transform, Load) Power BI.

Key Features of Power Query

1. User-Friendly Interface

Power Query is a data transformation tool that provides a user interface where users can apply changes using menus or buttons, rather than writing code.

It logs all the steps in the Applied Steps pane, which can be edited or deleted easily.

2. Unlimited Data Sources Connecting

Connects to various data sources like Excel files, CSV, SQL databases, APIs, SharePoint, web pages and many more.

3. M Language (Power Query Formula Language)

At the heart of it, Power Query uses a flexible and powerful scripting language M. An advanced user could write or edit M code to develop more complex analyses.

4. Reusable Queries

When a query is created, it can be utilized repeatedly when data changes. This can save lots of time and work.

5. Data Shaping

Use features such as filtering rows, removing duplicates, changing data types, pivoting/unpivoting columns, splitting/merging columns, and more

The reason why Power BI is also that important

1. Preparation of Data is 80% Beach Work

No matter what kind of data analysis or reporting you are doing, data cleaning and preparation usually take most of your time. Power Query automates and eases this crucial stage, enabling analysts to dive deep into insights rather than cleaning up every now and then.

2. Empowers Non-Technical Users

With Power Query, even someone who is not a programmer can write complex transformations over data within an easy-to-use UI. This democratises data analytics throughout an organisation.

3. Handles Complex Data Models

Power Query allows you to merge, append, and establish relationships between tables, making it the perfect tool for providing some horizontal logic across your fact tables, whether they be residing in flat files or a traditional relational database, saving time, increasing accuracy, and giving an edge to all your reports.

4. Repeatable and Refreshable

From here Power Query is all set up, and you can refresh your data with a click. It is most helpful for reports that recur and the data in it changes from time to time.

5. Seamless Integration with Power BI

Power Query is so deeply integrated in Power BI Desktop. You use Power Query to create the queries, which feed directly into your data model.

Real-Life Example

Suppose you are provided with monthly sales data from several regional offices, and each is in a different format (e.g., CSV or Excel). Power Query allows you to:
You do not have to write a single line of code for all this.

Final Thoughts

Power Query is way more than just a feature… It’s the power behind effective data transformation across Power BI. Power Query is key to enhancing your efficiency and reporting accuracy, whether you’re a greenhorn or senior data analyst.

If you are looking into a career in data analytics or business intelligence, Power Query is the single most important tool to learn. But, make sure you start right by joining Power BI training in Hyderabad, which provides practical experience of real-world projects and coaching from the experts. This was probably one of the most valuable investments into my data career.

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