Summary  
This chapter explains how to implement join transformations (inner, left/right/full outer, cross) to merge multiple datasets based on key conditions and then apply derived column transformations to compute additional fields.  

General domain of usage  
ETL data integration pipelines

Data often resides in multiple tables or sources, making it essential to **merge information** to derive meaningful insights. In this chapter, you'll learn how to use joins in Azure Data Factory (ADF) Data Flows to merge datasets efficiently

ADF supports several types of joins:

- **Inner Join**: combines rows where keys match in both datasets;
- **Left Outer Join**: includes all rows from the left dataset and matching rows from the right;
- **Right Outer Join**: includes all rows from the right dataset and matching rows from the left;
- **Full Outer Join**: includes all rows from both datasets, with nulls for unmatched rows;
- **Cross Join**: produces a Cartesian product of both datasets.

### How to Combine Data with Joins in ADF  

1.  Create **new** Data Flow or use **existing** one;
2. Drag two **Source Transformations** onto the Data Flow canvas and connect them to the respective SQL tables;
3. Drag a **Join Transformation** from the toolbox onto the canvas and connect the two sources to it;  
4. In the Join Transformation settings, select the **Join Type**  and set the **Join Condition**; 
5. Add a **Derived Column Transformation** or **any other transformation** after the join to get some insights;  
5. Add a **Sink Transformation** to store the output;   
6. Validate the Data Flow configuration to ensure everything is correct.


In the Join Transformation settings, which of the following must you define?

Master the essentials of data engineering with Microsoft Azure in this comprehensive course. Starting with foundational concepts like cloud computing, resource management, and storage solutions, you'll progress to hands-on training in Azure Data Factory (ADF), mastering ETL/ELT workflows, and advanced data transformations. Through practical examples and real-world problem-solving, you'll gain the skills to design, implement, and optimize scalable data solutions using Azure.

Have a deep dive into the foundational concepts and tools required to begin your journey with Microsoft Azure. You'll explore cloud computing basics, learn how to organize and manage resources efficiently using Azure Resource Groups, and set up core services like Blob Storage and Azure SQL Database. Additionally, you’ll secure sensitive data with Azure Key Vault and gain an overview of Azure Data Factory (ADF) to understand its role in modern data engineering workflows.

Explore the core functionalities of Azure Data Factory (ADF), a powerful tool for creating and automating ETL/ELT workflows. In this section, you'll learn how to configure services, datasets, and pipelines while exploring key activities such as Copy, Lookup, and Script. You'll also work with advanced features like the ForEach activity for iteration and pipeline parameters for flexibility. 

This section focuses on leveraging Azure Data Factory's data flow capabilities to transform and enrich your data. You'll learn to connect various data sources, create derived columns, and merge datasets using joins. Practical examples will guide you through filtering, sorting, conditional splits, and aggregating data to generate meaningful insights. By mastering these transformations, you'll be equipped to build robust, efficient ETL workflows tailored to real-world data engineering challenges.

Apply your knowledge to real-world data engineering scenarios in this hands-on section. You'll tackle practical challenges like loading and analyzing data for different categories, identifying patterns, and assessing financial health using advanced ADF workflows. By solving these problems, you'll gain experience in designing end-to-end data solutions, working with diverse datasets, and generating actionable insights.

Combining Data with Joins

How to Combine Data with Joins in ADF