Get in Touch

Course Outline

Fundamentals of Azure Data Factory

  • Comprehensive summary of Azure Data Factory
  • Core operational frameworks and system architecture
  • Operational applications and strategic advantages of ADF

Establishment of the ADF Operational Environment

  • Provisioning and setup of ADF instances
  • Interface navigation within the Azure Data Factory management console
  • Explanation of integration runtime components

Management of Datasets and Linked Services

  • Definition and specification of datasets and linked services
  • Establishment of connections to diverse data repositories
  • Configuration of authentication protocols and secure data channels

Construction of Data Pipelines

  • Foundational elements and pipeline structure
  • Development of basic data pipelines using standard activities
  • Implementation of pipelines for data transfer operations

Data Flow and Transformation Workflows

  • Overview of the data flow processing engine
  • Execution of data transformation tasks
  • Design and execution of data flow transformation logic

Pipeline Scheduling and Trigger Management

  • Automating pipeline execution through trigger mechanisms
  • Utilization of time-based and event-driven triggers
  • Supervision of pipeline status and interpretation of execution logs

Diagnostic Procedures and Exception Management

  • Troubleshooting techniques for pipelines and data flows
  • Deployment of error capture and automatic retry strategies
  • Integration of exception handling into existing pipeline designs

Performance Enhancement Strategies

  • Industry standards for maximizing pipeline efficiency
  • Configuration of concurrency and data partitioning
  • Refinement of overall pipeline throughput and speed

Security Protocols and System Monitoring

  • Protection of ADF resources through role-based access control
  • Enforcement of data encryption and secure transmission standards
  • Oversight of data pipelines using native monitoring utilities and alert systems

Complex Integration and Advanced Use Cases

  • Interoperability with complementary Azure services
  • Management of intricate data integration workflows
  • Development of comprehensive, end-to-end data integration systems

Conclusions and Recommended Follow-Up Actions

Requirements

  • Fundamental comprehension of cloud computing principles
  • Working knowledge of data integration standards and ETL (Extract, Transform, Load) methodologies

Intended Audience

  • Data engineering specialists
  • Data analytics professionals
  • ETL development staff
  • Information technology personnel
 14 Hours

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories