In the era of data-driven decision-making, efficient data integration is the linchpin for organizations aiming to derive meaningful insights from a plethora of data sources. Microsoft's Azure Data Factory emerges as a powerful cloud-based data integration service designed to streamline the process of collecting, transforming, and moving data from diverse origins to various destinations within the Microsoft Azure platform.
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Introduction to Advanced Azure Data Factory
Recap of Basic Azure Data Factory Concepts
Understanding the Need for Advanced Features in Complex Data Integration Scenarios
Overview of the Topics Covered in the Advanced Course
Data Transformation Activities
Working with Data Flow in Azure Data Factory
Implementing Complex Data Transformation Logic Using Data Flow
Using Data Flow Debugging and Profiling Tools
Performance Optimization Techniques for Data Flow Activities
Advanced Control Flow
Implementing Conditional and Iterative Control Flow in Azure Data Factory
Using Lookup and ForEach Activities for Dynamic Data Processing
Working with Expressions and Functions for Control Flow Logic
Managing Dependencies and Parallelism
Understanding Dependencies Between Data Pipelines and Activities
Implementing Event-Driven and Time-Based Scheduling
Configuring Parallel Execution for Data Processing Optimization
Error Handling and Monitoring
Implementing Fault Tolerance and Error Handling in Azure Data Factory
Handling Transient Errors and Retry Mechanisms
Monitoring and Logging Best Practices for Data Pipelines
Integrating with Azure Services
Working with Azure Functions in Azure Data Factory
Integrating Azure Machine Learning for Data Processing and Predictions
Using Azure Logic Apps for Workflow Automation
DevOps for Azure Data Factory
Implementing Version Control for Data Pipelines Using Azure Repos
Setting up CI/CD Pipelines for Azure Data Factory
Best Practices for Collaboration Between Development and Operations Teams
Security and Governance
Managing Access Control and Security for Azure Data Factory
Implementing Encryption and Data Protection in Data Pipelines
Complying with Data Governance and Regulatory Requirements
Advanced Data Integration Patterns
Implementing Data Partitioning and Incremental Data Loading
Working with Change Data Capture (CDC) and Slowly Changing Dimensions (SCD)
Data Synchronization and Data Replication Techniques
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