Azure Data Factory Overview English Introduction 2026

Azure Data Factory is a powerful and flexible platform that helps you design complex data integration and orchestration processes efficiently. With the latest updates and features in 2024, ADF offers even more possibilities to successfully implement your data projects.

You will need to work extensively with Azure Data Factory if you want to become certified in Azure.

Introduction to Azure Data Factory 2026

Azure Data Factory (ADF) in 2026! In this article/workshop, you will learn everything you need to know to optimally use Microsoft’s powerful data integration and orchestration platform.

What is Azure Data Factory?

Azure Data Factory is a fully managed data integration service that enables you to create, schedule, and orchestrate ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. ADF - Azure Data Factory supports you in extracting data from various sources, transforming it, and loading it into different destinations, whether in the cloud or on-premises.

Why is Azure Data Factory one of the most important services?

Azure Data Factory is like an all-rounder for your data. It ensures that all your data sources work together, your data is clean and organized, and everything ends up where it should. That’s why ADF is one of the most important services for data processing. It makes your life easier and helps you get the most out of your data.

Simple Azure Data Factory Example Imagine you have many different data sources: perhaps some data in an Excel spreadsheet, others in a SQL database, and even more data in various cloud services like Google Analytics or Salesforce. Now you want to bring all this data together, clean it up a bit, and then put it in a central database so you can analyze it and create beautiful reports from it. Sounds complicated, right?

This is where Azure Data Factory comes in. Think of ADF as a very smart and industrious robot that collects all this data for you, sorts it, cleans it, and brings it to the right place.

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Key Features of Azure Data Factory

1. Data Integration

Diverse data sources: ADF supports a wide range of data sources, including SQL databases, NoSQL databases, file systems, SaaS applications, and more. Connectors: Pre-defined connectors facilitate seamless integration with various data sources and destinations.

2.Pipelines and Activities (If you don’t know what pipelines are, I’ve written a small explanation at the bottom)

Pipelines Create pipelines that contain a logical group of activities to define data integration and transformation processes. Activities Activities are the building blocks of a pipeline and can copy data, transform it, store it, and much more.

3. Orchestration and Automation Scheduling Schedule pipelines by time or based on events to automatically execute data processes. Monitoring Use comprehensive monitoring and diagnostic features to track the status and performance of your pipelines.

4. Data Flow

Mapping Data Flows With visually designable data flows, you can perform complex data transformations without code. Code-based Transformations Use familiar technologies like Spark and Databricks for flexible and powerful transformations.

5. Hybrid Data Integration Self-hosted Integration Runtime Enables integration of on-premises data sources and hybrid environments. Azure Integration Runtime Use the Azure environment for data movement and transformation in the cloud.

6. Security and Governance Encryption Data is encrypted during transmission and at rest to ensure security. Azure Data Factory (ADF) offers comprehensive security and governance features to ensure your data is always protected and your data processes are compliant. Here are the key aspects:

Security in Azure Data Factory

1. Encryption

Data in transit: ADF uses TLS (Transport Layer Security) to ensure data is securely transferred between various services. Data at rest: Data stored in Azure is encrypted by default to protect it from unauthorized access. 2. Access Control

Role-Based Access Control (RBAC) With RBAC, you can precisely control who can access your ADF resources. You can assign specific roles and permissions to ensure that only authorized users can make changes or view data. Azure Active Directory (AAD) Integration: ADF integrates seamlessly with Azure AD, enabling centralized management of user identities and stronger authentication.

3. Network Security

VNet Integration: You can integrate ADF into your virtual network (VNet) to restrict access to your data sources and destinations to internal networks. Private Endpoints: Private endpoints enable secure connections to ADF by avoiding communication over the public internet. Governance in Azure Data Factory

1. Compliance and Certifications Azure Data Factory meets numerous global, industry-specific, and regional compliance requirements. These include ISO/IEC 27001, HIPAA, FedRAMP, SOC 1, and SOC 2. This ensures that ADF meets the highest standards for data protection and security.

2. Monitoring and Logging Azure Monitor: With Azure Monitor, you can monitor the activities and performance of your data pipelines in real time. This helps you quickly identify and resolve issues. Activity Log and Pipeline Run History These features provide detailed logs of all performed activities and pipeline executions, which are essential for audits and troubleshooting.

3. Data Lineage and Data Catalog Data Lineage: ADF provides data lineage to track the lifecycle of data from source to destination. This helps you understand the origin, movement, and transformations of your data. Azure Data Catalog: ADF can be integrated with Azure Data Catalog to manage metadata and facilitate the discovery of data sources.

4. Policies and Management Azure Policy: With Azure Policy, you can define and enforce governance policies to ensure all ADF resources comply with corporate standards. Tagging: Tagging helps you categorize and manage ADF resources, which is particularly useful for cost management and resource allocation.

Access Control Use role-based access control (RBAC) and Azure Active Directory (AAD) for secure data access.

Use Cases for Azure Data Factory

1. ETL and ELT Processes

Automate the extraction, transformation, and loading of large amounts of data from different sources into data warehouses or data lakes.

2. Data Migration Migrate data between different storage locations, e.g., from on-premises databases to the Azure cloud.

3. Data Integration Combine data from various sources for analytics, reporting, and machine learning.

4. Data Preparation Prepare and clean data for further analysis and machine learning.

5. Real-time Data Processing Process data in real time and integrate it into analysis and reporting processes.

News and Improvements 2024 / 2025

Enhanced Machine Learning Integration ADF now offers improved integration with Azure Machine Learning to seamlessly embed ML models in data pipelines.

Optimized User Interface The user interface has been further improved to provide an even more intuitive and user-friendly experience.

Extended Connectors New connectors enable even more comprehensive integration with modern data sources and destinations.

Small Explanation: What are Pipelines?

Think of a pipeline as a kind of production line for your data. Just like in a factory where a product is assembled step by step, pipelines in Azure Data Factory (ADF) process your data in multiple steps. Here’s a simple explanation:

What does a pipeline do?

A pipeline in ADF is a group of activities that work together to accomplish a task. Think of it like a to-do list that is worked through point by point. Each activity in the pipeline is a single step that does something with the data, for example:

Collect data: The first activity could retrieve data from a SQL database. Transform data: The next activity could clean and convert this data, for example by removing unnecessary information or converting the data into a different format. Store data: The final activity could store the processed data in a data lake or another database.

Why are pipelines useful?

Automation: You can automate recurring tasks so they run repeatedly without your intervention. Efficiency: Data processing becomes more efficient because the steps are clearly defined and executed sequentially. Flexibility: You can customize the pipeline to do exactly what you need. Different activities can be combined to accomplish complex tasks. Monitoring: You can easily monitor what happens at each stage of the pipeline and ensure everything runs smoothly.

Example of a Pipeline

Suppose you work for an e-commerce company and want to create a sales report daily:

Collect data: Get the sales data from the order system. Clean data: Remove erroneous entries and duplicate data. Analyze data: Calculate daily sales, profits, and other important metrics. Store data: Save the finished reports in a database or send them via email to management. Each of these tasks is an activity in your pipeline, and ADF ensures they are executed in the correct order.