What Are Queries in Power BI?

Building a powerful report starts long before you drag a single visual onto your dashboard or report canvas in Power BI. Behind every meaningful chart and KPI is a query, which performs the essential work of fetching and shaping your raw data. This article will show you what Power BI queries are, how they work, and how you can use them to transform messy data into something you can actually analyze.

What Is a Query in Power BI?

A query in Power BI is like a recipe for your data. It not only tells Power BI what data to get but also specifies the exact source of that data and how to transform it before loading it for analysis. It's a set of sequential steps - a script - that gets executed every time you refresh your report, ensuring your data is always clean, consistent, and ready for visualization.

Every interaction within the Power Query Editor - clicking, filtering, transforming, or even combining data from different sources - is recorded in a query. This query can be rerun repeatedly, making the reporting process much more efficient and reliable.

The Role of the Power Query Editor

While queries are technically written using a code-based scripting language known as M language, the good news is that you rarely have to write M script code directly. The Power BI interface includes the Power Query Editor, which is user-friendly. This Editor acts as your graphical interface where you can create and edit your queries using menus and clicks. By interacting with your data - removing columns, applying filters, or merging tables - the Editor writes the corresponding M query steps for you, making data preparation accessible to everyone, regardless of technical skills.

The Anatomy of a Power BI Query

Every query has a similar structure, which you can view in the Power Query Editor. It’s a series of 'applied steps' implemented sequentially on your data to transform it.

1. Data Source

All queries originate from a data source. This could be an Excel workbook, a Google Sheet, a SQL database, a SharePoint folder, a webpage tab, or a CSV file. The first step for a query is connecting to the source to fetch your raw data and transform it into the final table ready for visualizations.

2. Applied Steps

• Filtering Rows and Columns: This allows you to trim your dataset to only retain the information you need for your report. For example, you might only include sales from the last quarter or remove personally identifiable information from your dataset.
• Change Data Types: This is one of the most critical steps in a query. It ensures that text values are treated as text, number values as numbers, and dates as dates. Without this step, you wouldn’t be able to perform calculations like averages or sums, as numbers might just be read as text.
• Split & Merge Columns: You may find that you need to split one column into several (e.g., splitting a full name into first and last names) or merge columns (e.g., creating a new column by combining city and zip code). The Power Query provides easy tools to do this without having to write any formulas.
• Append and Merge Queries: This combines your datasets. "Appending" means stacking tables on top of each other (like copying and pasting January sales data into a table that already has February sales). "Merging" is like a VLOOKUP in Excel, where you join two tables based on a common column (like Product ID).
• Creating Custom and Calculated Columns: You can create new columns from scratch using simple UI-based formulas or by writing some advanced ones with M language. This is very powerful because it allows you to create metrics not available in your raw data, like calculating profit by subtracting cost from revenue.

Putting It Into Practice: Building a Simple Query

The best way to understand queries is to create one yourself. Here is a simple walkthrough that turns a messy campaign spreadsheet into a clean table ready for analysis.

Our Goal: Analyze the Performance of Marketing Campaigns

We want to calculate the Return on Ad Spend (ROAS) from raw data exported from a system. This allows us to effectively allocate our budget.

1. Connect to a Data Source: First, use the Get Data function to connect to your campaign sheets file. This step loads the data into Power Query for transformation.
2. Remove Unnecessary Columns: The raw dataset may contain columns we don't need. Remove these, such as 'Notes' and 'Creator ID', to clean up your data.
3. Filter by Campaign Status: We only want to analyze 'Active' campaigns. Use the Filter function on the 'Status' column to show all the rows that have 'Active' as their status.
4. Correct Data Types: The spend and revenue columns should be treated as Currency. Change their data type from text to Fixed Decimal Number so we can perform mathematical operations on them.
5. Create a Custom Column: Now, add a New Custom Column called 'ROAS' by dividing 'Revenue' by 'Spend'. Use the Add Custom Column button for this.
6. Rename the Query and Load Data: Finally, give your query a sensible name like 'Campaign Performance'. Click "Close & Apply". This loads the cleaned-up data into your Power BI report, and it’s now ready to be used for visualization.

Why Do Power Queries Matter?

Queries are the foundation of reliable and efficient reporting. Without a well-structured query, your reports may not perform well. Automation and repeatability are key. Once you build a query, it can be refreshed automatically, meaning less manual effort and more reliable data updates.

By only loading the data you truly need, your Power BI reports will be faster and more responsive, which is crucial for a good user experience.

Final Thoughts

Understanding how to build effective queries in Power BI is the key to unlocking reliable, automated, and advanced analytics. They are the backbone of any great reporting dashboard. Learning to master them is a game-changer for anyone working with data.

We know that while Power BI is a fantastic tool, the learning curve can be steep, especially when you just want to get quick answers from your data. This is why we built Graphed. It turns hours of data wrangling into quick questions, enabling you to create live dashboards and get answers simply by describing what you want.