Comprehensive Guide to Transformations in Azure Data Factory

Azure Data Factory offers versatile transformations for efficient data workflows, including source extraction, aggregation, joins, and more. Its intuitive interface simplifies complex ETL processes, ensuring scalable data integration with diverse sources and destinations.

Azure Data Factory (ADF) is a powerful data integration service that allows you to create, schedule, and orchestrate data workflows at scale. ADF provides a rich set of data transformation capabilities that help you process and transform your data to meet your business needs. In this blog, we will explore the various transformations available in Azure Data Factory and how you can use them to create efficient data workflows.

Introduction

Data transformation is a critical component of any data pipeline, enabling you to manipulate and restructure your data to fit your analytical and operational requirements. Azure Data Factory supports a wide range of transformations that can be applied using both Data Flow and Mapping Data Flow activities.

Data Flow vs. Mapping Data Flow

Before diving into the specific transformations, it’s essential to understand the difference between Data Flow and Mapping Data Flow in ADF.

• Data Flow: Provides a visual and code-based interface to create complex ETL processes. It allows for row-level transformations using a variety of operations.
• Mapping Data Flow: A code-free design interface where you can build and manage your data transformation logic. Mapping Data Flow is part of the Azure Data Factory and Synapse Pipelines, offering a visual way to design your data transformations.

Transformation	Description	Use Case
Source	Reads data from various data sources like Azure Blob Storage, Azure Data Lake, SQL databases, etc.	Extract data from multiple sources to process and analyze.
Derived Column	Creates new columns or modifies existing ones using expressions.	Standardize data formats, create calculated fields.
Filter	Filters rows based on a specified condition.	Remove unwanted data from the pipeline, such as filtering out records with null values.
Select	Selects specific columns from the dataset.	Reduce the dataset size by selecting only the necessary columns.
Aggregate	Aggregates data by performing operations like sum, average, min, max, and count.	Summarize data, such as calculating total sales or average order value.
Join	Joins data from two or more streams based on a condition.	Combine data from multiple sources, such as joining customer data with order data.
Union	Combines data from multiple streams into a single stream.	Merge datasets from different sources.
Lookup	Enriches data by looking up values in another dataset.	Add additional data fields, such as looking up customer details based on customer ID.
Conditional Split	Splits data into multiple streams based on conditions.	Route data to different paths based on specific conditions, such as separating transactions.
Exists	Checks if rows from one stream exist in another stream.	Filter out records that do not exist in a reference dataset.
Pivot	Converts row data into columns.	Reshape data for reporting purposes, such as pivoting sales data by region.
Unpivot	Converts columns into rows.	Normalize data for processing, such as converting year-wise columns into a single year column.
Flatten	Transforms hierarchical structures into a flat structure.	Simplify nested data, such as flattening JSON or XML data.
Surrogate Key	Generates unique surrogate keys for rows.	Add unique identifiers to records, such as generating IDs for new data entries.
Window	Applies window functions over a specified range of rows.	Perform calculations over a set of rows, such as running totals or moving averages.
Assert	Validates data against specified conditions.	Ensure data quality by asserting business rules, such as checking for non-negative values.
Cross Join	Produces a Cartesian product of two datasets.	Generate combinations of data, such as pairing all products with all stores.
Rank	Assigns ranks to rows within a partition.	Rank data, such as assigning ranks to students based on their scores.
External Call	Invokes external services during data processing.	Call external APIs or services to enrich data or perform operations outside ADF.
Alter Row	Specifies row-level insert, update, delete, and upsert policies.	Implement row-level changes in databases based on conditions.
Destination	Writes data to various destinations such as databases, data lakes, etc.	Load transformed data into target storage or database systems.

You can access the Transformation From Azure Data Factory -> Launch Studio -> Data Flow -> Add Transformation.

For More Info: Azure Data Factory Transformations which gives the step-by-step implementation of each Transformations.

Azure Data Factory provides a comprehensive set of transformations that enable you to build robust and scalable data workflows. By leveraging these transformations, you can efficiently process and manipulate your data to meet your business needs. Whether you are performing simple data transformations or complex data integrations, ADF’s rich feature set ensures that you can achieve your goals with ease.

Happy Data Transforming!