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
Testimonials (2)
All of the training was great. Especially liked the training documentation to reference. Looking forward to the Advanced Training, when we are ready.
Amy Gregg - Qualfon
Course - Boomi Integration Fundamentals
I've find out new interesting things about Lambda and Serverless