Azure Data Factory: Data Integration and Management

• Basic knowledge of cloud computing concepts
• Familiarity with data integration and ETL processes

Audience

• Data engineers
• Data analysts
• ETL developers
• IT professionals

Azure Data Factory (ADF) is a cloud-based data integration service provided by Microsoft Azure. It enables users to create, orchestrate, and manage data pipelines that move and transform data from various sources.

This instructor-led, live training (online or onsite) is aimed at beginner-level data engineers who wish to use Azure Data Factory to implement scalable and efficient data integration solutions in the cloud.

By the end of this training, participants will be able to:

• Understand the core concepts and architecture of Azure Data Factory (ADF).
• Learn to create, schedule, and monitor data pipelines.
• Integrate with various data sources, perform data transformation, and manage data flows.
• Implement best practices for error handling and performance optimization.
• Secure and monitor data pipelines for production environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Azure Data Factory

• Overview of Azure Data Factory
• Key concepts and architecture
• Use cases and benefits of ADF

Setting Up Your ADF Environment

• Creating and configuring an ADF instance
• Navigating the Azure Data Factory portal
• Understanding integration runtimes

Working with Datasets and Linked Services

• Defining datasets and linked services
• Connecting to different data sources
• Configuring authentication and secure connections

Creating Data Pipelines

• Pipeline basics and components
• Creating simple data pipelines with activities
• Building a pipeline for data movement

Data Flow and Transformation Activities

• Introduction to data flow
• Data transformation activities
• Designing and running data flow transformations

Scheduling and Triggering Pipelines

• Scheduling pipeline runs with triggers
• Using tumbling window and event-based triggers
• Monitoring pipeline activity and understanding output logs

Debugging and Error Handling

• Debugging pipelines and data flows
• Implementing error handling and retry mechanisms
• Adding error handling to existing pipelines

Performance Optimization

• Best practices for optimizing data pipelines
• Understanding and configuring parallelism and partitioning
• Optimizing pipeline performance

Security and Monitoring

• Securing ADF with access control and permissions
• Implementing data encryption and secure data transfer
• Monitoring data pipelines with built-in tools and alerts

Advanced Scenarios and Integration

• Integrating ADF with other Azure services
• Handling complex data integration scenarios
• Creating an end-to-end data integration solution

Summary and Next Steps