Azure Data Factory (ADF) Training
Build Scalable Cloud Data Integration & ETL Pipelines with Azure Data Factory
Build practical, job-ready skills in Azure Data Factory, cloud ETL, data integration and modern data engineering. Learn ADF pipelines, activities, linked services, datasets, Integration Runtime, Mapping Data Flows, incremental loads, CDC, parameterization, triggers, monitoring, security and CI/CD through hands-on exercises.
Enquire About TrainingCourse Overview
Azure Data Factory Training is designed to take learners from the fundamentals of cloud-based data integration to advanced ETL and data engineering solutions using Azure Data Factory.
You will work with pipelines, activities, linked services, datasets, Integration Runtime, Copy Data, Mapping Data Flows, parameters, variables, expressions, triggers, incremental loads, CDC, metadata-driven pipelines, monitoring, security, Azure Key Vault, Git and CI/CD.
Course Highlights
Gain practical Azure Data Factory expertise through a comprehensive curriculum covering cloud data integration, ETL pipelines, data movement, transformations, orchestration, incremental processing, monitoring, security, deployment and real-world project implementation.
ADF Pipelines & Orchestration
Learn how to design pipelines, activities, dependencies, control flow and reusable orchestration patterns for enterprise data workloads.
ETL & Data Integration
Build data movement and integration solutions across databases, files, cloud storage, APIs and enterprise data sources.
Linked Services & Datasets
Configure connections, datasets and reusable data integration components for different Azure and external data platforms.
Mapping Data Flows
Develop transformation workflows using Mapping Data Flows for filtering, joining, aggregating, derived columns, lookups and data cleansing.
Azure Integration
Integrate ADF with Azure SQL, ADLS Gen2, Blob Storage, Synapse, Databricks and other cloud and enterprise data services.
Incremental Loads & CDC
Implement watermark-based processing, change detection, incremental loading and production-oriented data ingestion patterns.
Git & CI/CD
Understand source control, collaboration, deployment pipelines, environment management and CI/CD practices for Azure Data Factory.
Trainer Experience
Learn from an industry professional with 19+ years of experience in database, ETL, data engineering, analytics and modern data technologies.
Why Choose Our Azure Data Factory Training?
The training follows a structured path from Azure Data Factory fundamentals to advanced enterprise data integration, with emphasis on practical ETL tasks and production-oriented pipelines.
Hands-On Development
Practice ADF pipelines, activities, datasets, linked services, expressions and transformations through practical exercises.
Beginner to Advanced
Progress from Azure Data Factory fundamentals to dynamic pipelines, incremental processing, monitoring and CI/CD.
Real-World Data Engineering
Understand batch ingestion, incremental loads, CDC, metadata-driven pipelines, APIs and enterprise integration scenarios.
Pipeline Performance
Learn how to design efficient pipelines, manage parallelism, optimize activities and control data movement costs.
Azure Data Platform Integration
Work with Azure SQL, ADLS Gen2, Blob Storage, Synapse, Databricks and other data platforms.
Production Deployment Practices
Learn Git integration, CI/CD, environment configuration, deployment and production support practices for ADF.
Who Can Join?
Freshers & Graduates
Students and graduates who want to build a career in cloud data engineering, ETL and Azure data integration.
SQL & Database Professionals
SQL developers, database professionals and developers looking to move into Azure cloud data engineering.
ETL Developers
SSIS and traditional ETL developers who want to transition to cloud-based data integration using Azure Data Factory.
Analytics Professionals
BI and analytics professionals who want to understand cloud data movement, transformation and orchestration.
Azure Professionals
Azure professionals who want to expand their skills into data integration, ETL and Azure Data Factory.
Data Engineers
Data engineers who want to build reusable, scalable and production-ready Azure data pipelines.
Career Opportunities
Azure Data Factory Developer
Develop pipelines, activities, data integration workflows and enterprise ETL solutions using Azure Data Factory.
Azure Data Engineer
Build cloud data ingestion, transformation, orchestration and integration solutions across Azure services.
ETL Developer
Design and implement batch, incremental and metadata-driven ETL workflows for enterprise data platforms.
Cloud Data Integration Developer
Integrate databases, files, APIs, cloud storage and SaaS sources using modern cloud data integration patterns.
Data Platform Engineer
Work on data platform integration, automation, monitoring, optimization and production engineering activities.
Cloud ETL Engineer
Develop scalable cloud-based ETL and ELT solutions using Azure Data Factory and related Azure data services.
What You Will Gain
Understand ADF architecture, pipelines, activities, datasets, linked services and Integration Runtime.
Build reliable data movement and integration pipelines across databases, files, APIs and cloud platforms.
Use Mapping Data Flows and pipeline activities for filtering, joins, lookups, aggregations and data cleansing.
Implement watermark-based loads, incremental processing and change-data-capture patterns.
Design parameterized, metadata-driven and reusable pipelines with monitoring and error-handling practices.
Apply Git, CI/CD, managed identity, Key Vault and secure deployment practices to ADF solutions.
Trusted Azure Data Factory Training
Learn from an experienced training institute with a practical, technology-focused approach to professional training.
100+
Training Programs
50+
Practical Learning Activities
20+
Technology & Data Platform Areas
19+
Years Industry Experience
STUDENT SUCCESS STORIES
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Upcoming Free Demo Class
📚 Course: Azure Data Factory (ADF) Training
📅 Start Date: 16 October 2026
🕒 Time: 09:00 PM - 10:00 PM IST
⏰ Class: 1 Hour
🗓️ Schedule: Weekdays-Online
⌛ Duration: 8 Weeks
💰 Course Fee: Contact us for Fee Details
🌐 Meeting Link: Contact us for zoom meeting link
Live Azure Data Factory Training Sessions
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Course Syllabus
Our Azure Data Factory syllabus progresses from cloud data integration fundamentals to advanced pipeline development, Mapping Data Flows, incremental processing, CDC, metadata-driven frameworks, monitoring, security, CI/CD and production deployment.
01. Azure Data Factory Fundamentals
FOUNDATION
- Introduction to Azure Data Factory
- What is cloud data integration?
- ADF architecture and major components
- Azure Data Factory workspace
- Authoring and monitoring experiences
- Data integration and ETL/ELT concepts
- ADF versus traditional ETL tools
- Azure data platform overview
- ADF development lifecycle
- Real-world data integration scenarios
02. ADF Architecture & Core Components
ARCHITECTURE
- Pipelines
- Activities
- Linked services
- Datasets
- Integration Runtime
- Triggers
- Parameters and variables
- Expressions and dynamic content
- Pipeline dependencies
- Authoring and publishing concepts
03. Linked Services, Datasets & Integration Runtime
CONNECTIVITY
- Creating linked services
- Dataset design and configuration
- Azure SQL Database connectivity
- SQL Server connectivity
- Azure Blob Storage
- Azure Data Lake Storage Gen2
- File-based sources
- REST and HTTP-based sources
- Azure Integration Runtime
- Self-hosted Integration Runtime
- Integration Runtime architecture and use cases
04. Copy Data & Data Movement
DATA INGESTION
- Copy Activity fundamentals
- Source and sink configuration
- File-to-database ingestion
- Database-to-database movement
- Cloud storage ingestion
- Delimited, JSON, XML and Parquet files
- Schema mapping
- Column mapping
- Parallel copy concepts
- Fault tolerance and retry settings
- Staging concepts
05. Mapping Data Flows
TRANSFORMATION
- Mapping Data Flow fundamentals
- Source and sink transformations
- Select and derived column
- Filter and conditional split
- Join and lookup
- Aggregate and window transformations
- Exists and alter row
- Surrogate key concepts
- Data cleansing and standardization
- Data Flow parameters
- Debugging Mapping Data Flows
06. Parameters, Variables & Dynamic Expressions
DYNAMIC PIPELINES
- Pipeline parameters
- Dataset parameters
- Data Flow parameters
- Pipeline variables
- System variables
- Expression language fundamentals
- Dynamic content
- String and date functions
- File and folder expressions
- Dynamic SQL concepts
- Reusable parameterized pipelines
07. Control Flow Activities & Pipeline Orchestration
ORCHESTRATION
- Execute Pipeline activity
- Lookup activity
- ForEach activity
- Get Metadata activity
- Set Variable activity
- Filter activity
- If Condition activity
- Switch activity
- Until activity
- Wait activity
- Web activity
- Azure Function activity
- Stored Procedure activity
- Notebook activity concepts
08. Triggers, Scheduling & Event-Driven Pipelines
AUTOMATION
- Schedule triggers
- Tumbling window triggers
- Event-based triggers
- Storage event integration
- Trigger parameters
- Dependency handling
- Time-zone considerations
- Pipeline scheduling strategies
- Backfill and rerun concepts
- Event-driven data integration
09. Incremental Loads & Change Data Capture
INCREMENTAL PROCESSING
- Full load versus incremental load
- Watermark concepts
- High-water-mark implementation
- Last modified date patterns
- Lookup-driven incremental pipelines
- Change Data Capture concepts
- Insert, update and delete processing
- MERGE-based loading patterns
- Incremental file processing
- Restartable pipeline design
- Real-world incremental load scenarios
10. Metadata-Driven & Framework-Based Pipelines
ADVANCED ETL
- Metadata-driven architecture
- Configuration-table approach
- Generic ingestion pipelines
- Dynamic source and destination selection
- Parameter-driven processing
- ForEach-based framework design
- Control tables and audit tables
- Pipeline logging patterns
- Reusable ETL framework design
- Enterprise data ingestion patterns
11. Monitoring, Debugging & Performance Optimization
MONITORING
- ADF monitoring experience
- Pipeline run history
- Activity run details
- Debugging pipelines
- Failure analysis
- Retry and timeout configuration
- Data Flow monitoring
- Integration Runtime monitoring
- Pipeline performance optimization
- Parallelism and concurrency
- Cost-aware pipeline design
- Alerts and operational monitoring concepts
12. Security, Managed Identity & Azure Key Vault
SECURITY
- Azure identity fundamentals
- Managed identity
- Service principals
- Azure Key Vault integration
- Secure credential management
- Role-based access control
- Storage access security
- Database authentication
- Private endpoints and network concepts
- Secrets management best practices
- Production security considerations
13. Git, CI/CD & Production Deployment
DEVOPS & PRODUCTION
- Git integration with Azure Data Factory
- Branches and collaboration
- Feature development workflow
- Publish process
- Azure DevOps integration concepts
- CI/CD pipeline architecture
- Environment-specific configuration
- Deployment parameters
- Infrastructure and deployment automation concepts
- Production release management
- Rollback and recovery concepts
- Enterprise ADF best practices
Sample Azure Data Factory Training Videos
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Frequently Asked Questions
What is Azure Data Factory?
Azure Data Factory is a cloud-based data integration and orchestration service used to create and manage data movement and transformation pipelines.
Who is this Azure Data Factory Training for?
The course is suitable for beginners as well as SQL professionals, ETL developers, data engineers, Azure professionals, database developers and analytics professionals who want to build cloud data integration skills.
Do I need prior Azure Data Factory experience?
No. The curriculum starts with Azure Data Factory and cloud data integration fundamentals and progresses toward advanced pipeline development and production deployment.
Will I learn ETL and ELT?
Yes. The training covers practical ETL and ELT patterns including data movement, transformations, orchestration, incremental processing and integration with Azure data services.
Does the course cover Mapping Data Flows?
Yes. Mapping Data Flows are covered with transformations such as filter, join, lookup, aggregate, derived column, conditional split and data cleansing.
Will I learn incremental loads and CDC?
Yes. The course includes watermark-based incremental loading, change detection, insert/update/delete processing and production-oriented CDC patterns.
Will Azure Data Factory integrate with databases and files?
Yes. The curriculum includes database, file, cloud storage, REST and other external data integration scenarios using linked services, datasets and Integration Runtime.
Does the training cover Git and CI/CD?
Yes. Git integration, collaboration, publishing, Azure DevOps concepts, CI/CD and production deployment practices are included.
Is the training available online?
Yes. The course is designed for online and offline training as indicated in the course information.
Ready to Learn Azure Data Factory?
Learn Azure Data Factory step by step, practice real-world ETL and data integration scenarios and build scalable cloud data pipelines.
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