🧹 Data Cleaning & Transformation Using Power Query in Power BI

Raw data is rarely clean. Missing values, duplicate records, incorrect data types, and inconsistent formats can ruin your analysis and dashboards.

That’s where Power Query comes in.

Power Query is Power BI’s data preparation engine that allows you to clean, transform, and shape data before it reaches your data model.

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🔍 What Is Power Query?

Power Query is a no-code / low-code ETL tool inside Power BI used for:

• Data extraction
• Data transformation
• Data loading (ETL)

Open Power Query using:

Home → Transform Data

Every action is recorded as an Applied Step, making transformations repeatable and auditable.

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🗑️ Removing Duplicates in Power Query

Duplicate records often occur due to:

• Multiple data sources
• System errors
• Manual data entry

🔹 How to Remove Duplicates

1. Select the column(s)
2. Go to Home → Remove Rows → Remove Duplicates

Power Query keeps the first occurrence and removes others.

Best Practice:

• Remove duplicates before creating relationships
• Apply on unique keys (Order ID, Customer ID)

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🚫 Removing Null or Blank Values

Null values can break calculations and visuals.

🔹 Remove Null Rows

1. Select the column
2. Click the filter dropdown
3. Uncheck (null)

🔹 Replace Null Values

• Go to Transform → Replace Values
• Replace nulls with:
• 0 for numeric fields
• Unknown for text fields

Tip: Never blindly remove nulls—understand why they exist.

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🔢 Changing Data Types (Critical Step)

Incorrect data types lead to:

• Wrong aggregations
• Broken DAX formulas
• Visual errors

🔹 Common Data Types

Data	Recommended Type
Sales Amount	Decimal / Whole Number
Order Date	Date
Customer Name	Text
IsActive	True / False

🔹 How to Change Data Type

1. Select the column
2. Choose Data Type from the toolbar
3. Select the correct type

Always fix data types in Power Query, not in the data model.

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🔗 Merging Tables in Power Query (SQL JOIN)

Merging combines tables horizontally using a common key.

🔹 Example Use Cases

• Sales table + Customer table
• Orders + Product details

🔹 Steps to Merge Tables

1. Home → Merge Queries
2. Select primary & secondary tables
3. Select matching columns
4. Choose join type:
   • Left Outer (most common)
   • Inner
   • Full Outer
5. Expand required columns

Power Query supports SQL-style joins without writing SQL.

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➕ Appending Tables (Stacking Data)

Appending combines tables vertically.

🔹 Example Use Cases

• Monthly sales files
• Year-wise data
• Multiple region datasets

🔹 Steps to Append Queries

1. Home → Append Queries
2. Select:
   • Two tables
   • Three or more tables
3. Load combined table

Ensure:

• Same column names
• Same data types

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🧱 Building Clean, Analysis-Ready Datasets

✅ Data Quality Checklist

• No duplicate primary keys
• Correct data types
• Consistent column naming
• No unnecessary columns
• Clean date tables

📌 Recommended Flow

Raw Data → Power Query Cleaning → Data Model → DAX → Visuals

Clean data results in faster performance, simpler DAX, and reliable dashboards.

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🚀 Power Query Best Practices

• Clean data once, reuse everywhere
• Rename queries meaningfully
• Disable load for staging queries
• Avoid unnecessary steps
• Maintain query folding where possible

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🔮 What’s Next?

👉 Part 5: Creating Your First Power BI Report

• Adding visuals
• Filters & slicers
• Dashboard formatting
• Publishing reports

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Visit: www.Datahark.in

What is Power BI? A Complete Guide to Microsoft’s Business Intelligence Tool

Getting Started with Power BI: A Beginner’s Guide to Data Visualization
Data Preparation in Power BI: Power Query Editor, Data Transformation & DAX Guide

🔗 Connecting to Data Sources in Power BI (Excel, SQL, Web & APIs)

 📌 Introduction

Connecting to data is the first and most critical step in any Power BI project. Power BI supports hundreds of data sources, making it one of the most flexible BI tools available today.

In this post, you’ll learn how to:

• Connect Power BI to Excel, CSV, and Text files
• Connect to SQL Server and databases
• Pull data from web sources and APIs
• Understand Import vs DirectQuery vs Live Connection

This guide is ideal for beginners transitioning from Excel to Power BI.

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📊 Types of Data Sources in Power BI

Power BI data sources fall into four main categories:

Category	Examples
File-based	Excel, CSV, Text, XML
Database	SQL Server, MySQL, Oracle
Cloud	Azure SQL, SharePoint, Google Analytics
Online / Web	APIs, Web pages, REST endpoints

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📁 Connecting to Excel, CSV & Text Files

🔹 Connecting to Excel Files

Excel is the most common data source for Power BI users.

Steps:

1. Open Power BI Desktop
2. Click Get Data → Excel
3. Select your file
4. Choose tables or sheets
5. Click Load or Transform Data

✅ Best for:

• Small to medium datasets
• Business users & analysts
• Financial and sales reporting

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🔹 Connecting to CSV & Text Files

CSV and TXT files are lightweight and widely used.

Steps:

1. Get Data → Text/CSV
2. Select file
3. Preview delimiter & data format
4. Load or transform data

⚠️ Common issues:

• Incorrect delimiters
• Encoding problems
• Date format mismatches

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🗄️ Connecting to SQL Server & Databases

Power BI integrates seamlessly with databases.

🔹 SQL Server Connection

Steps:

• Get Data → SQL Server
• Enter Server name

• Choose:

• Windows Authentication
• Database Authentication
• Select Import or DirectQuery
• Load data

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🔹 Other Supported Databases

• MySQL
• PostgreSQL
• Oracle
• Snowflake
• Azure SQL Database

✅ Best practice:

• Write optimized SQL queries
• Avoid SELECT *
• Pre-aggregate data when possible

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🌐 Connecting to Web Data & APIs (Basic)

Power BI can pull data from web URLs and REST APIs.

🔹 Web Data (Simple)

Used for public web pages or downloadable datasets.

Steps:

1. Get Data → Web
2. Paste URL
3. Choose Table or JSON output
4. Transform data in Power Query

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🔹 APIs (Beginner Level)

APIs usually return data in JSON format.

Example:

https://api.example.com/data

Power BI converts JSON into tables automatically using Power Query.

⚠️ Limitations:

• Authentication complexity
• API rate limits
• Requires Power Query transformations

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🔄 Import vs DirectQuery vs Live Connection

Choosing the right connection mode is crucial for performance.

🔹 Import Mode

• Data is loaded into Power BI
• Fastest performance
• Supports full DAX functionality

✅ Best for:

• Small to medium datasets
• Offline analysis

❌ Not real-time

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🔹 DirectQuery Mode

• Queries data directly from source
• Real-time data access
• Limited DAX & modeling features

✅ Best for:

• Large datasets
• Real-time dashboards

❌ Slower visuals
❌ Dependent on source performance

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🔹 Live Connection

• Used with:

• Power BI datasets
• Azure Analysis Services
• No data stored in Power BI

✅ Best for:

• Enterprise BI
• Centralized data models

❌ No Power Query
❌ No calculated columns

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🔍 Comparison Table

Feature	Import	DirectQuery	Live
Performance	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Real-time	❌	✅	✅
DAX Support	Full	Limited	Limited
Data Size	Medium	Large	Enterprise

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📌 Best Practices for Data Connections

• Clean data at source level when possible
• Use Import unless real-time is required
• Optimize SQL queries
• Avoid unnecessary columns
• Use Power Query transformations wisely

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🚀 What’s Next?

In the next part, we’ll cover:

👉 Part 4: Data Cleaning & Transformation Using Power Query

You’ll learn how to:

• Remove duplicates & nulls
• Change data types
• Merge and append tables
• Build clean, analysis-ready datasets

Data Preparation in Power BI: Power Query Editor, Data Transformation & DAX Guide

Data preparation is one of the most critical steps in building powerful and insightful dashboards in Power BI. Before creating visualizations, it's essential to clean, transform, and structure your data for accuracy and performance. In this blog, we’ll walk through the key elements of data preparation in Power BI, including an introduction to Power Query Editor, common data transformation techniques, and how to create calculated columns and measures using DAX.

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What is Data Preparation in Power BI?

Data preparation in Power BI involves importing raw data from various sources, cleaning it, reshaping it, and transforming it into a structured format ready for analysis. This process ensures your reports and dashboards reflect accurate and actionable insights.

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Introduction to Power Query Editor in Power BI

The Power Query Editor is a built-in data transformation tool in Power BI Desktop. It helps you connect to data sources, clean and reshape the data, and define rules for how the data should be loaded into your Power BI model.

Key Features of Power Query Editor:

• Intuitive user interface with step-by-step transformations
• Applied Steps pane to track each data change
• Query dependencies and relationships view
• Powerful M language for advanced transformations

Accessing Power Query Editor:

1. Open Power BI Desktop
2. Click Home > Transform Data
3. The Power Query Editor window will open with your imported dataset

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Common Data Transformation Techniques

Power BI supports a wide range of transformation techniques to prepare your data. Here are some essential ones:

1. Merge Queries (Joins)

• Combine data from two or more tables using common columns
• Types of joins: Left, Right, Inner, Outer, Full
• Example: Merge a Customer table with Orders to get customer-wise orders

Step: Home > Merge Queries > Choose Join Type > Match Columns

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2. Append Queries

• Stack rows from one table below another (like UNION in SQL)
• Useful when combining data from multiple sources with the same structure

Step: Home > Append Queries > Select tables to append

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3. Pivot and Unpivot

• Pivot: Turn unique values from a column into new columns
• Unpivot: Transform columns into rows for normalization

Step: Transform > Pivot Column or Unpivot Columns

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4. Filtering and Sorting Data

• Remove unnecessary or duplicate rows
• Apply filters to focus on specific data
• Sort columns to improve data readability

Step: Use filter icons or right-click column > Remove Duplicates

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5. Changing Data Types and Renaming Columns

• Set correct data types (Text, Number, Date, etc.) for consistency
• Rename columns for clarity

Step: Transform > Data Type / Right-click > Rename

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Cleaning and Shaping Data

Data cleaning ensures your dataset is free from errors, inconsistencies, and irrelevant information.

Common Cleaning Techniques:

• Remove blank rows or columns
• Replace errors and null values
• Trim whitespace or unwanted characters
• Split columns using delimiters
• Group data by key fields

All these steps are available under Transform and Home tabs in Power Query Editor.

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Creating Calculated Columns and Measures Using DAX

Once your data is loaded into Power BI’s data model, you can create Calculated Columns and Measures using DAX (Data Analysis Expressions) to enhance your analysis.

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What is DAX?

DAX is a powerful formula language in Power BI that allows you to create custom calculations, aggregations, and business logic.

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1. Calculated Columns

A Calculated Column is created row by row for each record in a table. It’s useful when you need a new field derived from existing data.

Example:

FullName = Customers[FirstName] & " " & Customers[LastName]

Use Case: Combine two fields, classify categories, or create flags.

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2. Measures

Measures perform aggregations like SUM, AVERAGE, COUNT, etc., and are calculated dynamically in visuals.

Example:

TotalSales = SUM(Sales[Amount])

Use Case: Total sales, average profit, percentage share, KPIs

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3. Popular DAX Functions for Beginners

• CALCULATE() – Modify filter context
• IF() – Conditional logic
• FILTER() – Apply row-level filtering
• RELATED() – Fetch data from related tables
• DISTINCTCOUNT() – Count unique values

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Best Practices for Data Preparation in Power BI

• Always clean your data before modeling or visualization
• Avoid excessive calculated columns; use measures for aggregations
• Use naming conventions for tables and fields
• Minimize data load by removing unwanted columns and rows
• Apply transformations in Power Query to reduce memory usage

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Conclusion

Effective data preparation in Power BI is the foundation for successful reporting and data analysis. The Power Query Editor helps you clean and shape raw data, while DAX allows you to add powerful logic and calculations. Together, they turn complex data into clean, structured, and insightful dashboards.

Whether you're building your first report or optimizing existing ones, mastering data preparation will significantly enhance your Power BI skills.

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Keywords:

• Data Preparation in Power BI
• Power Query Editor in Power BI
• Data transformation techniques in Power BI
• How to clean data in Power BI
• Create calculated columns in Power BI
• Power BI DAX tutorial
• Power BI pivot and unpivot
• Power BI merge and append queries
• Shaping data in Power BI
• Data modeling Power BI

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Search Description:

Master data preparation in Power BI with this step-by-step guide on using Power Query Editor, transforming data, and creating calculated columns and measures using DAX.