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 Duration 14 hours

Course Outline

Introduction to Azure Data Factory

  • Comprehensive overview of Azure Data Factory
  • Core concepts and architectural components
  • Key use cases and strategic advantages of ADF

Configuring Your ADF Environment

  • Provisioning and configuring an ADF instance
  • Exploring the Azure Data Factory user interface
  • Comprehending integration runtimes

Managing Datasets and Linked Services

  • Defining datasets and configuring linked services
  • Establishing connections to diverse data sources
  • Setting up authentication protocols and secure connections

Building Data Pipelines

  • Foundations of pipeline structure and components
  • Constructing basic data pipelines using activities
  • Developing pipelines for effective data movement

Data Flow and Transformation Processes

  • Foundations of data flow
  • Executing data transformation activities
  • Designing and executing data flow transformations

Pipeline Scheduling and Triggers

  • Automating pipeline execution using triggers
  • Utilizing tumbling window and event-based triggers
  • Monitoring pipeline activity and analyzing output logs

Debugging and Error Management

  • Troubleshooting pipelines and data flows
  • Implementing error handling strategies and retry logic
  • Enhancing existing pipelines with robust error handling

Performance Tuning

  • Best practices for optimizing data pipeline efficiency
  • Configuring parallelism and partitioning for improved throughput
  • Refining overall pipeline performance

Security and Oversight

  • Enhancing ADF security through access control and permission management
  • Applying data encryption and secure data transfer methods
  • Utilizing built-in tools and alerts to monitor data pipelines

Complex Scenarios and Integration

  • Connecting ADF with other Azure platform services
  • Addressing intricate data integration challenges
  • Constructing a complete, end-to-end data integration solution

Recap and Future Steps

Requirements

  • Fundamental understanding of cloud computing principles
  • Working knowledge of data integration and ETL workflows

Intended Audience

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

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