Module 1: Introduction to Azure and Cloud Computing

1.1 Cloud Computing Fundamentals

1.1.1 Overview of Cloud Computing Concepts

1.1.2 Cloud Deployment and Service Models

1.1.3 Benefits of Cloud Computing

1.2 Introduction to Microsoft Azure

1.2.1 Overview of Microsoft Azure

1.2.2 Key Azure Services and Components

1.2.3 Azure Architecture and Resource Management

Module 2: Getting Started with Azure Data Factory

2.1 Introduction to Azure Data Factory

2.1.1 Overview of Azure Data Factory

2.1.2 Key Features and Capabilities of ADF

2.1.3 Common Data Integration Use Cases

2.2 Azure Data Factory Components

2.2.1 Pipelines, Datasets, and Activities

2.2.2 Linked Services and Integration Runtimes

2.2.3 Navigating the Azure Data Factory Interface

Module 3: Creating and Managing Data Pipelines in ADF

3.1 Building Data Pipelines

3.1.1 Creating Your First Data Pipeline

3.1.2 Configuring Pipeline Activities and Datasets

3.1.3 Connecting Azure Development Concepts with Data Integration

3.2 Managing and Monitoring Pipelines

3.2.1 Publishing and Executing Pipelines

3.2.2 Monitoring Pipeline Runs

3.2.3 Troubleshooting Pipeline Execution Issues

3.3 Configuring Pipeline Triggers

3.3.1 Creating Schedule Triggers

3.3.2 Configuring Event-Based Triggers

3.3.3 Managing Trigger Execution

Module 4: Data Integration and Transformation in ADF

4.1 Connecting Data Sources and Destinations

4.1.1 Configuring Source Connections

4.1.2 Configuring Destination Connections

4.1.3 Managing Linked Services and Datasets

4.2 Data Transformation Using Data Flows

4.2.1 Introduction to Mapping Data Flows

4.2.2 Using Data Flow Activities

4.2.3 Applying Data Transformation Rules

4.3 Data and Schema Mapping

4.3.1 Understanding Data Mapping

4.3.2 Configuring Schema Mapping

4.3.3 Handling Data Types and Schema Changes

Module 5: Workflow Orchestration and Data Movement

5.1 Managing Data Movement

5.1.1 Introduction to Copy Activities

5.1.2 Configuring Data Copy Operations

5.1.3 Managing Data Movement Between Systems

5.2 Working with Azure Integration Runtime

5.2.1 Understanding Integration Runtime Types

5.2.2 Configuring Azure Integration Runtime

5.2.3 Selecting the Appropriate Runtime for Data Movement

5.3 Monitoring Data Movement

5.3.1 Monitoring Copy Activity Execution

5.3.2 Reviewing Data Flow Execution Details

5.3.3 Troubleshooting Data Movement Failures

Module 6: Working with Data Storage Solutions in ADF

6.1 Integrating Azure Blob Storage

6.1.1 Connecting Azure Blob Storage to ADF

6.1.2 Configuring Datasets for Blob Storage

6.1.3 Importing and Exporting Data

6.2 Connecting Azure SQL Database and Data Lakes

6.2.1 Configuring Azure SQL Database Connections

6.2.2 Integrating Data Lake Storage

6.2.3 Moving Data Between Databases and Storage Services

6.3 Managing Azure Data Lake Storage Gen2

6.3.1 Understanding Data Lake Storage Gen2

6.3.2 Configuring Data Lake Access

6.3.3 Managing Data Storage and Integration Workflows

Module 7: Implementing Security and Access Control in ADF

7.1 Securing Data

7.1.1 Understanding Data Security in Transit and at Rest

7.1.2 Protecting Credentials and Connection Details

7.1.3 Applying Data Security Best Practices

7.2 Authentication and Authorization

7.2.1 Configuring Authentication Methods

7.2.2 Managing Access to Azure Data Factory

7.2.3 Using Managed Identities and Secure Connections

7.3 Azure Role-Based Access Control

7.3.1 Understanding Azure RBAC

7.3.2 Assigning Roles and Permissions

7.3.3 Reviewing Access Control Best Practices

Module 8: Advanced Azure Data Factory Features

8.1 Parameterization in Data Pipelines

8.1.1 Understanding Parameters and Variables

8.1.2 Creating Reusable Pipeline Components

8.1.3 Implementing Dynamic Pipeline Configurations

8.2 Custom Activities and Azure Functions

8.2.1 Understanding Custom Activities

8.2.2 Integrating Azure Functions with ADF

8.2.3 Executing Custom Processing Tasks

8.3 Error Handling and Logging

8.3.1 Configuring Pipeline Error Handling

8.3.2 Implementing Logging and Failure Notifications

8.3.3 Troubleshooting Pipeline Failures

Module 9: Automation and Optimization in ADF

9.1 Automating Data Pipeline Execution

9.1.1 Scheduling Automated Pipeline Runs

9.1.2 Configuring Trigger-Based Automation

9.1.3 Managing Dependencies Between Pipelines

9.2 Performance Optimization

9.2.1 Identifying Pipeline Performance Bottlenecks

9.2.2 Optimizing Data Copy and Transformation Activities

9.2.3 Improving Pipeline Execution Efficiency

9.3 Best Practices for Data Integration

9.3.1 Designing Efficient Data Integration Workflows

9.3.2 Managing Resources and Costs

9.3.3 Applying Reliability and Maintainability Best Practices

Module 10: Azure Data Factory Implementation and Practical Projects

10.1 Reviewing Key Concepts

10.1.1 Reviewing Azure and ADF Architecture

10.1.2 Revisiting Pipeline Development and Data Transformation

10.1.3 Reviewing Security, Monitoring, and Optimization

10.2 Common Scenarios and Use Cases

10.2.1 Building Data Integration Workflows

10.2.2 Automating Data Transfers Between Azure Services

10.2.3 Implementing Real-World Data Integration Scenarios

10.3 Practice Exercises and Real-World Project

10.3.1 Designing and Building an End-to-End Data Pipeline

10.3.2 Integrating Multiple Data Sources and Destinations

10.3.3 Implementing Monitoring and Error Handling

10.3.4 Testing and Optimizing the Completed Solution