How to Join Tables in Power BI

In Power BI, tables are a basic step to create the data model. It is like a spreadsheet, which stores data in rows and columns, with each column representing a different field. Every table contains specific, structured information that keeps the data organized, clean, and efficient. Joining tables is very important when you are creating reports, as it provides a clear view of data, and it is very easy to manage. In this blog, let us explore the role of tables and the different ways to join them, along with examples.

Table of Contents:

• What Are Tables in Power BI?
• Why Join Tables in Power BI?
• Different Types of Table Joins in Power BI
• Power BI Joins vs Relationships: What’s the Difference?
• How to Join Tables in Power BI (Step-by-Step)?
  • Method 1: Join Tables with Power Query in Power BI
  • Method 2: Join tables using DAX in Power BI
• Performance Comparison: Power Query Join vs DAX Join
• Power BI Table Join Performance Tips & Considerations
• When Not to Join Tables in Power BI?
• Best Practices for Joining Tables in Power BI
• Fixing Common Power BI Join Errors
• Use Cases for Table Joins in Power BI
• Conclusion

What Are Tables in Power BI?

Tables are a structured way of representing data in rows and columns, and they keep your data clean and well-structured. Tables in Power BI are just like Excel spreadsheets. Tables in Power BI can be imported from various sources and are stored in Power BI.

Why Join Tables in Power BI?

Joining tables in Power BI allows you to create and manage your data from multiple sources.

• It helps in creating reports and dashboards.
• It helps in performing a detailed study of data.
• It helps in preventing data loss during analysis.
• It helps in filtering data across connected datasets.

Different Types of Table Joins in Power BI

Power BI offers many types of joins that are similar to SQL:

• Inner Join: Inner Join allows only rows that have matching values in both tables.
• Left Join: Left join in Power BI is used to return all rows from the left table and matched rows from the right table.
• Right Join: Right Join allows rows from the right table and matched rows from the left table.
• Full Outer Join: Full Outer Join allows you to return all rows when there is a match in either of the tables.

Power BI Joins vs Relationships: What’s the Difference?

How to Join Tables in Power BI (Step-by-Step)?

Let’s join the two tables on the “CustomerID.“

• A Sales table.
• A Customer table.

1. Customer Table:

2. Sales Table:

Method 1: Join Tables with Power Query in Power BI

Power Query Editor is an ETL tool (Extract, Transform, and Load) that is used to extract and reshape the data. Here, we have used Power Query Editor to join tables based on a common column.

Step 1: Load the dataset into Power BI

Click on Home > Get Data > Text/CSV

1. Customer Table:

This is the Customer table that looks after loading it into the dataset

2. Sales Table

This is the Sales table that looks after loading it into the dataset

Step 2: Go to Power Query Editor
Click on “Transform Data.”

Step 3: Power BI Merge Queries

Select the Sales Table and click “Merge Queries.”

Step 4: In the Merge Window

Select Sales[CustomerID] and Select Customer[CustomerID] and choose the kind of join you want to apply (we will choose Left Outer Join because we want to keep all records from the sales table and matching customer details, which makes sure that no sales data is lost even if some customer information is missing).

Step 4: Expand the Joined Table

After combining tables, a new column will appear in the table

• Click on the expand icon
• Select the field you want to bring
  • CustomerName
  • Country

Step 5: Result

Explanation: Here, this table is formed by joining the Sales and Customer tables on CustomerID.

Method 2: Join tables using DAX in Power BI

DAX stands for Data Analysis Expressions, which is a powerful language in Power BI used for custom calculations and aggregation of data. We can also use DAX to join tables.

Step 1: Load the dataset into Power BI
Click on Home>Get Data>Text/CSV

1. Customer Table:

2. Sales Table

Step 2: Go to the Modelling Tab and write the formula below

Sales_Customer_Join = 
SELECTCOLUMNS (
    ADDCOLUMNS (
        Sales,
        "CustomerName", RELATED(Customer[CustomerName]),
        "Country", RELATED(Customer[Country])
    ),
    "SaleID", Sales[SaleID],
    "CustomerID", Sales[CustomerID],
    "Product", Sales[Product],
    "Amount", Sales[Amount],
    "SaleDate", Sales[SaleDate],
    "CustomerName", [CustomerName],
    "Country", [Country]
)

Explanation: Here, this table is formed by joining the Sales and Customer tables on CustomerID.

Performance Comparison: Power Query Join vs DAX Join

Both Methods are correct and efficient for joining rows; however, let’s look at a comparison of Power Query Join vs DAX Join performance.

Power BI Table Join Performance Tips & Considerations

1. Before joining two tables together, try to remove unwanted rows and columns, which helps to improve performance.
2. Make sure the column through which you are joining tables together does not contain any blank or null values.
3. Remove duplicate data before joining the table, as it can lead to wrong results.
4. Using a Left join in Power BI over a Full Outer Join leads to performance issues.
5. Only select the columns you want to join. Avoid joining unnecessary fields, as it increases memory usage.

When Not to Join Tables in Power BI?

While joining tables is a common approach to build datasets, there are scenarios where using Power BI Table Join may not be ideal:

1. Circular Dependencies
   Creating too many joins or bidirectional relationships may cause circular dependency issues, which are easier to manage with relationships instead of actual joins.
2. Large Datasets with Repeating Data
   If you’re using a Left join in Power BI on large fact tables, it can duplicate values and slow down performance. In this case, creating relationships is more optimal.
3. Unclean Data
   If either table contains nulls, blanks, or mismatched data types, the result of a join will be unreliable or cause errors.
4. Calculated Measures Instead of Flattened Data
   If your goal is to calculate KPIs or metrics across tables, you should use relationships with DAX measures instead of creating flattened joined tables.

Best Practices for Joining Tables in Power BI

• Name your table: Try to name your table when working with large and multiple tables, as this will avoid confusion.
• Check Data Types: Always double-check the data types of the table that you plan to join.
• Limit the Tables: Working on multiple tables can slow down the performance, so try to keep the number of tables fewer to increase the performance.
• Use DAX: For transforming your data, you can use a DAX formula to make your data more efficient.

Fixing Common Power BI Join Errors

• Different Data Types: There is no possibility of joining tables when two tables have columns with different data types. But you can fix this error by transforming the data types of columns in Power Query.

• Duplicate values: If there are duplicate values in the column you want to join, then the join operation will not give you the correct result. You can fix this problem by removing all the duplicate values in the Power Query Editor.

• Blank or Null values: If your table contains blank values or NULL values, then the joined table will also have blank values. To avoid this problem, try to remove blank values or try to fill them by applying statistical formulas like average, mean, mode, and median.

• Case Sensitive: Make sure your records are either in uppercase or in lowercase. You can resolve this by making all records in uppercase or lowercase with the help of Text.Upper() or Text.Lower().

• Incorrect Join Type: Applying the wrong join type in tables produces incorrect results, so make sure you are applying the correct type of joins and that it’s fetching you the correct result.

Use Cases for Table Joins in Power BI

1. Simplify export-ready datasets
Use Power BI merge queries to flatten your data model and prepare clean datasets for export, analysis, or integration with other tools.

2. Add descriptive fields to fact tables
Use Join Tables in Power BI to bring in customer names, product details, or region info into a sales or transaction table for better reporting.

3. Combine data from multiple sources
When working with data from different systems, use a Power BI Table Join to combine tables in Power BI into a single, unified view.

4. Clean and reshape the data before loading
Use Power Query Join in Power BI to merge tables during the ETL process, ensuring clean, well-structured data before it’s loaded into the model.

5. Create dynamic calculated columns
You can join tables using DAX in Power BI to build custom columns that respond to slicers and filters within your report.

Conclusion

Joining tables in Power BI is one of the most effective ways to work with data from multiple sources. It allows you to combine related information and create more meaningful insights. By using the correct join type and identifying the right common columns, you can ensure data accuracy and performance. Properly joined tables make the data model cleaner, faster, and easier to manage. This not only improves report quality but also helps in making better business decisions.

To learn more about Power BI and its functions, check out this Power BI Course and also explore Power BI Interview Questions prepared by industry experts.

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How to Join Tables in Power BI – FAQs