Combining Data from Multiple Tables
Learn how Power Query combines data from multiple tables using Append and Merge. Practice stacking similar tables, matching related columns, selecting join keys, and creating an analysis-ready dataset in Power BI.
Concept 1
Why Combine Data from Multiple Tables?
Real-world analytics rarely starts with one perfect table. Sales may be stored separately by month, region, branch, or business unit. Power Query allows analysts to combine these sources into structures that are easier to analyze.
| Source | Customer | Sales |
|---|---|---|
| January | Amit | 85000 |
| January | Priya | 62000 |
| February | Rahul | 45000 |
| February | Neha | 92000 |
Interactive: What are you trying to combine?
January and February contain the same columns and represent the same type of sales records. What operation is appropriate?
Which Power Query operation is generally used to combine tables that have the same type of columns by stacking their rows?
Complete the activity and related MCQ to unlock the next concept.
Combining Data in Power BI with Power Query
Power Query provides tools for combining data from multiple sources. Two important operations are Append Queries and Merge Queries. Understanding the difference between these operations is essential for Power BI data preparation and analytics workflows.
Append Queries in Power BI
Append Queries is useful when similar tables need to be combined by stacking their records. For example, January Sales and February Sales can be appended when both tables contain compatible sales fields.
Merge Queries in Power BI
Merge Queries is used when related information exists in different tables. A matching field such as CustomerID can be used to connect sales records with customer attributes such as Region or Segment.
Data preparation with multiple tables
A practical Power BI data preparation workflow may clean source data, append similar datasets, merge related tables, and validate the resulting structure before reporting and visualization.