Azure Data Factory (ADF): Build Scalable Data Pipelines | Udemy [Update 08/2025]
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Build, Orchestrate, and Secure Data Pipelines with Azure Data Factory for Cloud, Hybrid, and Multi-Cloud Integration

What you'll learn
Explain the fundamentals of Azure Data Factory (ADF) and its role within the Azure cloud ecosystem.
Understand cloud computing concepts, services, and data types relevant to enterprise data integration.
Identify and configure core components of ADF including linked services, datasets, pipelines, and triggers.
Perform data ingestion and migration tasks such as copying data across Azure Blob Storage, ADLS Gen2, SQL databases, and on-premises sources.
Analyze and manage copy activity behavior to optimize data transfer efficiency and reliability.
Implement parameterization in ADF pipelines using linked services, datasets, and variables for reusability and dynamic configurations.
Execute advanced copy operations such as bulk data transfers, file count-based activities, and multi-file ingestion scenarios.
Leverage stored procedures and SQL queries within ADF pipelines to transform and manage data flows.
Convert and transform structured data formats (e.g., CSV to JSON) using ADF data flows.
Apply security best practices by integrating Azure Key Vault for secrets management and secure credential handling.
Design and execute different data loading strategies including full loads, incremental (delta) loads, and hybrid approaches.
Integrate APIs and external services within ADF pipelines to extend data movement and transformation capabilities.
Orchestrate hybrid and multi-cloud data workflows by connecting ADF with AWS, Google Cloud, and on-premises systems.
Schedule, monitor, and automate pipelines using triggers and scheduling features in ADF.
Apply transformation logic using data flows, including join operations, to prepare data for analytics and downstream systems.

A warm welcome to the Azure Data Factory (ADF): Build Scalable Data Pipelines course by Uplatz.

What is Azure Data Factory

Azure Data Factory (ADF) is Microsoft's cloud-based ETL (Extract, Transform, Load) and data integration service. It enables organizations to move, transform, and orchestrate data from multiple sources, whether on-premises, in the cloud, or across different platforms.

It serves as the data pipeline service within Azure, allowing data to be connected, cleaned, and delivered to systems such as data lakes, data warehouses, business intelligence platforms, and machine learning pipelines.

How Azure Data Factory Works

Azure Data Factory follows a workflow approach with four main stages:

1. Connect to Data Sources (Extract)

ADF connects to more than 100 data sources using linked services, such as SQL Server, Azure Blob Storage, Amazon S3, Google Cloud Storage, Salesforce, and SAP. Data is ingested either in batches or real time.

2. Prepare and Transform Data (Transform)

ADF uses Data Flows (a visual, no-code transformation interface) or custom activities such as SQL scripts, Spark jobs, Databricks notebooks, and stored procedures. Transformations may include joins, filtering, aggregations, format conversions (CSV to JSON, Parquet, etc.), and data cleansing.

3. Move and Load Data (Load)

Data is loaded into target systems including Azure SQL Database, Azure Synapse Analytics, Azure Data Lake, Cosmos DB, or external storage systems. It supports full loads, incremental (delta) loads, and streaming ingestion.

4. Orchestrate and Monitor Pipelines

Workflows are organized into pipelines that contain one or more activities. Triggers allow scheduling or event-based execution. ADF includes built-in monitoring and logging to track performance, identify errors, and analyze throughput.

Core Components of ADF

Pipelines: Logical groups of activities that define a workflow

Activities: Individual steps such as copy, transform, or execute stored procedure

Datasets: References to data structures such as tables or files

Linked Services: Connection details to data sources

Data Flows: Visual interface to build transformation logic

Integration Runtime (IR): The compute engine that executes data movement and transformations, available as cloud or self-hosted

Why Use Azure Data Factory

Fully managed and serverless with automatic scaling

Supports hybrid and multi-cloud data integration

Low-code/no-code development experience with option for advanced coding

Enterprise-grade security and governance through Azure Key Vault and RBAC

Prepares data pipelines for advanced analytics, reporting, and machine learning workloads

Azure Data Factory - Course Curriculum

Topic 1: Foundations of Azure & ADF

Session 1 - Introduction to Azure Data Factory

Session 2 - Cloud Computing Part-1

Session 3 - Cloud Computing Part-2

Session 4 - Cloud Services

Session 5 - Types of Data

Topic 2: Core Components of ADF

Session 6 - Top Components of ADF PART-1

Session 7 - Top Components of ADF PART-2

Topic 3: Data Copy & Migration Basics

Session 8 - Case Study-1: Copying the Data from Blob Storage to Blob Storage

Session 9 - Azure BLOB Storage to ADLS Gen2 Copy

Session 10 - Copy Multiple Files from Azure BLOB Storage to ADLS Gen2

Session 11 - Copy Data from Azure Blob to SQL DB

Session 12 - Copy Data Tool

Session 13 - Copy Activity Behavior PART-1

Session 14 - Copy Activity Behavior PART-2

Topic 4: Parameterization in ADF

Session 15 - Parameterized Linked Services PART-1

Session 16 - Parameterized Linked Services PART-2

Session 17 - Parameterized Dataset and Pipeline

Topic 5: Advanced Copy Operations

Session 18 - Copy Bulk Data from SQL Database to Blob Storage PART-1

Session 19 - Copy Bulk Data from SQL Database to Blob Storage PART-2

Session 20 - Copy Activity on the Basis of File Counts in Source

Topic 6: Stored Procedures & Transformations

Session 21 - Understanding Stored Procedure on Azure Cloud

Session 22 - Copy of the Data Using Stored Procedure and SQL Query

Session 23 - Conversion of CSV to JSON Using ADF PART-1

Session 24 - Conversion of CSV to JSON Using ADF PART-2

Session 25 - Copy File (JSON to CSV)

Topic 7: Security & Key Management

Session 26 - Azure Key Vault Service

Topic 8: Loading Strategies

Session 27 - Full Load and Delta Load PART-1

Session 28 - Full Load and Delta Load PART-2

Session 29 - Full Load and Delta Load PART-3

Topic 9: Hybrid Data Integration

Session 30 - Copy Data from On-Premise to Cloud in ADF PART-1

Session 31 - Copy Data from On-Premise to Cloud in ADF PART-2

Topic 10: API Integration & Variables

Session 32 - Integration of API with ADF PART-1

Session 33 - Integration of API with ADF PART-2

Session 34 - Pipeline Variable PART-1

Session 35 - Pipeline Variable PART-2

Topic 11: Multi-Cloud Integrations

Session 36 - Integration of AWS with Azure

Session 37 - Integration of ADF with Google Cloud Storage

Topic 12: Scheduling & Orchestration

Session 38 - Triggers in ADF

Session 39 - Schedule Trigger in Azure Data Factory PART-1

Session 40 - Schedule Trigger in Azure Data Factory PART-2

Topic 13: Data Transformations

Session 41 - Join Transformation Using Data Flows in ADF

This course takes you from the foundations of Azure and ADF through to advanced data integration techniques, covering real-world scenarios such as copying data across services, bulk migration, parameterization, hybrid integration, API connections, and multi-cloud workflows.

Through detailed sessions, hands-on examples, and case studies, you will gain both the conceptual understanding and practical expertise to build end-to-end data pipelines and orchestrate data flows in Azure.

By the end of this course, you'll be able to confidently design, deploy, and manage enterprise-grade data integration pipelines using ADF.

Who this course is for:
Data Engineers & ETL Developers - who want to design, build, and manage scalable data pipelines on Azure.
Cloud Engineers & Solution Architects - seeking to integrate on-premises and multi-cloud data sources into Azure-based analytics platforms.
Database Administrators (DBAs) - transitioning into cloud data management and automation using ADF.
Business Intelligence (BI) Developers & Analysts - who need to prepare, move, and transform data for reporting and analytics.
Big Data & Analytics Professionals - looking to orchestrate data workflows across heterogeneous systems.
Software Developers - who want to extend their skills into data integration and cloud-based data flows.
IT Professionals & System Integrators - involved in enterprise data migration, modernization, and cloud adoption projects.
Students & Beginners in Cloud Data Engineering - who wish to gain hands-on exposure to Azure Data Factory as a stepping stone into data engineering and cloud analytics careers.

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