Azure Data Factory Training - Table of Contents

1. Introduction to Azure Data Factory

What is Azure Data Factory (ADF)?

Importance of ADF in data integration

Key features and capabilities

Comparison with other ETL tools

2. ADF Architecture and Components

Overview of ADF architecture

Data pipeline components:

Pipelines

Activities

Datasets

Linked Services

Triggers

Integration Runtimes

3. Getting Started with ADF

Setting up an Azure account

Creating your first Data Factory

Navigating the ADF UI (Azure Portal)

4. Data Movement in ADF

Copy Activity: Moving data between sources

Data Flow vs Copy Activity

Supported data sources and destinations

Handling incremental data loads

5. Data Transformation Using Data Flows

Introduction to Data Flows

Mapping Data Flows vs Wrangling Data Flows

Data transformation operations (joins, aggregations, filters, etc.)

Debugging and monitoring data flows

6. Orchestration with Pipelines and Activities

Control flow activities (ForEach, If Condition, Switch)

Execution activities (Stored Procedure, Lookup, Web)

Handling dependencies between activities

Error handling and retries

7. Triggers and Scheduling in ADF

Types of triggers: Manual, Scheduled, Tumbling Window, Event-based

Scheduling best practices

Managing trigger failures

8. Monitoring and Debugging in ADF

Using ADF Monitoring tools

Debugging pipelines and activities

Logging and alerts

9. Security and Access Control

Role-Based Access Control (RBAC)

Managed Identity and Key Vault Integration

Secure data transfer with private endpoints

10. Integration with Other Azure Services

Azure Data Lake and Blob Storage

Azure SQL Database and Synapse Analytics

Azure Functions and Logic Apps

Power BI Integration

11. CI/CD and DevOps in ADF

Using Git integration in ADF

Deployment with Azure DevOps

Best practices for version control and releases

12. Real-World Use Cases and Hands-on Projects

Building a data pipeline from Azure Blob Storage to SQL

Data transformation using Mapping Data Flows

Automating ETL workflows with event triggers

End-to-end project: Ingest, transform, and visualize data