Course Outline

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

Requirements

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

Audience

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

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