Module 1: Introduction to Azure Data Factory

• What is Azure Data Factory and its role in the Azure data platform
• ADF architecture: pipelines, activities, datasets, linked services, and triggers
• ADF vs. Azure Synapse Pipelines vs. Databricks workflows: when to use each
• Navigating the ADF Studio interface: author, monitor, and manage tabs
• Setting up your ADF development environment and first data factory

Module 2: Linked Services, Datasets, and Integration Runtimes

• Creating and managing linked services for Azure and on-premises connections
• Configuring datasets: parameterized datasets and schema-on-read patterns
• Azure Integration Runtime vs. Self-hosted Integration Runtime: when to use each
• Installing and configuring self-hosted integration runtime for on-premises connectivity
• Managing integration runtime performance and scaling for large data volumes

Module 3: Copy Activity and Data Ingestion

• ADF Copy Activity: supported sources, sinks, and data movement patterns
• Configuring bulk data ingestion from SQL Server, Oracle, and SAP systems
• Incremental data copy patterns: watermark-based and change data capture approaches
• Copying data between Azure storage services: Blob, ADLS Gen2, and SQL Database
• Performance tuning Copy Activity: parallelism, staging, and data integration units

Module 4: Pipeline Orchestration and Control Flow

• Pipeline activities: Execute Pipeline, ForEach, If Condition, Until, and Switch
• Implementing dynamic pipelines with parameters and variables
• Pipeline chaining and modular pipeline design patterns
• Error handling: try-catch patterns with failure and completion activities
• Building metadata-driven pipelines for scalable, configuration-based orchestration

Module 5: Mapping Data Flows — Code-Free Transformation

• Introduction to Mapping Data Flows: visual ETL transformation canvas
• Core transformations: Select, Filter, Derived Column, Aggregate, and Join
• Advanced transformations: Pivot, Unpivot, Window, and Rank functions
• Conditional Split and Union for multi-branch data flow logic
• Data flow debugging, performance optimization, and cluster configuration

Module 6: Wrangling Data Flows and Power Query Integration

• Introduction to Wrangling Data Flows: Power Query-based transformation
• Cleaning and shaping data using Power Query M language in ADF
• When to use Wrangling Data Flows vs. Mapping Data Flows
• Handling semi-structured data: JSON, XML, and Parquet transformations
• Combining Wrangling and Mapping Data Flows in complex pipeline architectures

Module 7: Triggers and Pipeline Scheduling

• Schedule triggers: configuring time-based pipeline execution
• Tumbling window triggers: processing time-series data in fixed windows
• Event-based triggers: storage events and custom events for real-time pipelines
• Trigger dependencies and trigger chaining across multiple pipelines
• Managing trigger monitoring, debugging, and rerun strategies

Module 8: ADF Security, Monitoring, and Alerting

• ADF security: managed identity, Azure Key Vault integration, and RBAC
• Storing and rotating credentials securely with Azure Key Vault in ADF
• ADF Monitor: pipeline runs, activity runs, and trigger runs dashboards
• Setting up Azure Monitor alerts for pipeline failures and performance thresholds
• Diagnostic logging and integration with Log Analytics for operational insights

Module 9: CI/CD for Azure Data Factory

• Connecting ADF to Git: Azure DevOps Repos and GitHub integration
• ADF development workflow: feature branches, pull requests, and code reviews
• ADF CI/CD with Azure DevOps Pipelines: ARM template export and deployment
• Managing environment-specific configurations: dev, test, and production ADF deployments
• ADF deployment best practices: parameterization and linked service environment switching

Module 10: Capstone Projects and Career Readiness

• End-to-end project: build a complete data integration solution ingesting from multiple sources into Azure Data Lake
• Implementing a metadata-driven incremental pipeline with logging and alerting
• Portfolio preparation and GitHub documentation of ADF pipeline projects
• Interview preparation: ADF concepts, pipeline design, troubleshooting, and DP-203 exam scenarios
• Career pathways: Azure Data Engineer, Data Integration Engineer, Cloud ETL Developer

Experience in the Industry Gain knowledge from experts with practical Azure Data Factory project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.