Power BI Join Tables Understanding Relationships Easily

Combining data from multiple sources in Power BI could be an issue — and this guide addresses that! This guide dives into the world of Power BI join tables.

It shows you the most efficient and effective ways to consolidate and merge your data.

First…

Define Power BI Join Tables

Power BI join tables is the process of combining data from multiple tables based on common fields (or keys). It enables users to create relationships between tables, and gives room for comprehensive data analysis and data visualization within Power BI reports.

Why Join Tables in Power BI?

Join table in Power BI helps in consolidating data from multiple sources and creating comprehensive insights and visualizations. By merging related tables, users can establish relationships and enhance data analysis.

It gives room for the aggregation of data from multiple tables based on common fields, and it also enables users to perform complex calculations, comparisons, and filtering. The feature simplifies data modeling and enhances report creation. All these aid in a more comprehensive understanding of the data.

Pro-tip: Users have to ensure that the columns being used for the join have the same data type to avoid potential data mismatches before proceeding with the join.

What are the Different Types of Joins in Power BI?

Here are the different types of joins in Power BI:

• Inner Join: This join type only reruns the matching rows from both tables.
• Left Join: This join returns all rows from the left table and matching rows from the right table.
• Right Join: This join returns all rows from the right table, and matching rows from the left table.
• Full Join: This join returns all rows from both tables and includes non-matching rows.

How to Join Two Tables in Power BI?

Here are two ways to join tables in Power BI.

• On the relationships page
• With Power Query.

Joining With the Relationships Page

Using the relationships page in Power BI is the easiest way to join tables. If the table IDs are the same across tables, the relationships will automatically be picked up.

If Power BI doesn’t pick up on the relationships, create one. To do that, click and drag the column name from one table over to the other table.

To edit any relationship, double-click on the relationship line. A new window appears with several settings.

Joining With Power Query

There are cases where you’ll want to join a table in the data prep stages before it hits the data model. Joining a table with Power Query merges the table together with any number of columns you want to bring over.

Navigate to the Query Editor, right-click on the left side, and select New Query —> Merge as New.

Choose the tables you want to merge, and select the corresponding parent key and foreign key columns. Also, select the type of join as well. It could be Left Outer, Right Outer, Full Outer, Inner, Left Anti, and Right Anti.

After selecting OK, a new query will appear with the selected tables merged. To see everything, expand the ‘right’ side of the table.

To aggregate the ‘right’ table values, click the “Aggregate” radio button and select “Sum of Amount.”

A table similar to the one below appears on your screen.

Mastering Table Joins in Power BI for Clearer Data Visuals

Stage 1: Log into Power BI, enter your email, and click “Submit.”

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Create a Data Set and Select the Data Set to Use in the Sankey Chart

• Go to the left-side menu and click the “Create” button.
• Select “Paste or manually enter data“.

• We’ll use the sample data below for this example.

Quarters	Vendors	Market Share
Q1	Samsung	27.69
Q1	Apple	28.45
Q1	Xiaomi	11.8
Q1	Huawei	6.53
Q1	Oppo	5.3
Q1	Vivo	4.19
Q2	Samsung	28.14
Q2	Apple	27.58
Q2	Xiaomi	12.62
Q2	Huawei	6.17
Q2	Oppo	5.5
Q2	Vivo	4.21
Q3	Samsung	28.45
Q3	Apple	27.71
Q3	Xiaomi	12.9
Q3	Huawei	6
Q3	Oppo	5.29
Q3	Vivo	4.17
Q4	Samsung	27.97
Q4	Apple	27.62
Q4	Xiaomi	12.68
Q4	Huawei	5.17
Q4	Oppo	6.07
Q4	Vivo	4.66

• Paste the data table above into the “Power Query” window and name it “Market Share Analysis.” After that, select the “Create a dataset only” option.

• Navigate to the left-side menu, and click on the “Data Hub” option. Power BI will populate the data set list. If no data set has been created, you’ll get an error message.

• Click on “Create report.”

• Click on Market Share Analysis.

• Click on “Expand All.”

• After clicking on “Expand All,” you’ll see the chart metrics.

• Click on “Get more visuals.”

• You can search ChartExpo and select the Comparison Bar Chart.

• Click on “Add.”

• After that, you’ll see the Comparison Bar Chart in the visuals list.

• You can expand the chart space.

• After that, you can select the metrics of the data.

• Click on the second icon of Format Visuals and click on Visual.

• In Visual, click on License Settings and add the key. After adding a key, you’ll see the comparison bar chart.

• To add the header text, click on the General tab. After that, add the header text in Title.

• Here’s the final look at the Comparison Bar Chart in Power BI.

Insights

• Apple and Samsung are the top two vendors in terms of market share, with Samsung taking the lead.
• Huawei has been losing market share, while Xiaomi has been gaining market share.
• Vivo and Oppo have been relatively stable in terms of market share. In the last quarter, there has been a transition between Huawei and Oppo.

Improve Chart Accuracy with Power BI Join Tables:

1. Open your Power BI Desktop or Web.
2. From the Power BI Visualizations pane, expand three dots at the bottom and select “Get more visuals”.
3. Search for “Comparison Bar Chart by ChartExpo” on the AppSource.
4. Add the custom visual.
5. Select your data and configure the chart settings to create the chart.
6. Customize your chart properties to add header, axis, legends, and other required information.
7. Share the chart with your audience.

The following video will help you create a Comparison Bar Chart in Microsoft Power BI.

What are the Advantages of Merging Two Tables in Power BI?

• Comprehensive Analysis: Combining data gives room for comprehensive analysis. That involves consolidating data into a single dataset to enable users to analyze and visualize all relevant information.
• Data Consistency: Merging tables eliminates the need to manage separate datasets, and also reduces the risk of discrepancies and errors. This ascertains data consistency.
• Streamlined Reporting: Merged tables facilitate streamlined reporting, and that helps users create dashboards and reports using a unified dataset. This simplifies the reporting process.
• Relationships: Merging tables give users room to establish relationships between related data fields. It facilitates advanced analysis, filtering, and drilling down into data hierarchies.
• Enhanced Insights: Merging tables enables users to uncover insights that may not be apparent when analyzing individual datasets separately. This will, in turn, lead to deeper understanding and data-driven decision-making.

What are the Best Practices for Joining Two Tables in Power BI?

• Understand Data Relationships: You need to figure out the relationships between the tables and the key fields that establish these relationships.
• Choose the Right Join Type: Choose a join type based on the desired outcome and the relationship between the tables.
• Use Unique Keys: Join tables on unique keys or fields that guarantee a one-to-many or one-to-one relationship. This ascertains accurate and reliable results.
• Keep Data Model Simple: Do not create complex data models with unnecessary joins. Keep your data model as simple as possible while ensuring it meets reporting and analysis requirements.
• Apply Filtered Joins: Before joining large tables, consider applying filters to reduce the size of the tables. This will, in turn, improve performance and efficiency.
• Validate Results: To ascertain accuracy and completeness, you must validate the join’s results. Use test scenarios (or sample data) to verify that the join produces the expected outcome.

FAQs

How do I join two unrelated tables in Power BI?

Start by creating a relationship between the two unrelated tables based on a common field or use a DAX function like CROSSJOIN to generate all possible combinations.

How do I join two tables in DAX?

To join two tables in DAX, use functions like RELATED, LOOKUPVALUE, RELATEDTABLE, or CROSSJOIN. Your choice will be dependent on the relationship between the tables and the desired outcome.

What are the 5 different types of table joins?

The five different types of table joins are: inner join, left (outer) join, right (outer) join, full (outer) join, and cross join. Each of these serves different purposes in combining data.

Wrap Up

Power BI join tables involve combining data from multiple tables based on common fields (or keys). It allows users to merge related data sets into a single dataset, and also enables comprehensive analysis, visualization, and insights.

With Power BI join tables, users are allowed to establish relationships between tables for more robust data analysis.

To get the most out of the Power BI join tables, you have to document join logic, handle null values, and optimize performance. The logic behind each join should be documented. And that includes the reason for choosing a specific join type and the fields used for joining. All these help facilitate understanding and collaboration among team members.

You need to handle null values so as to avoid unexpected results (or errors) in the analysis.

Now you know what the Power BI join tables are, how will you integrate them into your data analysis (and visualization) process?