Overview of Azure Data Factory
Azure Data Factory is a cloud-based data integration service that allows you to create, schedule and manage data pipelines. With the increasing need to extract, transform, and load data from various sources to Azure, there is a high demand for skilled Azure Data Factory professionals. This course is designed to help you learn how to create data integration solutions using Azure Data Factory.
Stalwart Learning’s Azure Data Factory training program is a comprehensive course that covers all the key concepts of Azure Data Factory. You will learn how to create, configure and deploy pipelines that can extract, transform, and load data from various sources. You will also learn how to monitor, manage and troubleshoot data pipelines.
Throughout this course, you will get hands-on experience working with Azure Data Factory through various labs and exercises. You will learn how to:
- Configure and deploy pipelines
- Create data flow activities
- Monitor and troubleshoot pipelines
- Use Azure Data Factory with Azure Synapse Analytics and Azure Databricks
- Use Azure Data Factory with other Azure services such as Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database
- By the end of this course, you will be equipped with the knowledge and skills needed to build robust data integration solutions using Azure Data Factory.
Duration
24-40 Hours
Module 1: Introduction to Azure Data Factory
- Introduction to Azure Data Factory and its key components
- Understanding data integration and orchestration concepts
- Overview of data movement, transformation, and processing in Azure Data Factory
Module 2: Azure Data Factory Architecture and Components
- Exploring the architecture and components of Azure Data Factory
- Understanding pipelines, activities, and datasets in Azure Data Factory
- Configuring linked services for data source connections
- Managing and monitoring data factory resources
Module 3: Data Ingestion and Extraction
- Ingesting data from various sources into Azure Data Factory
- Configuring data extraction from on-premises and cloud-based sources
- Handling different file formats and data types
- Implementing data validation and error handling
Module 4: Data Transformation and Processing
- Transforming data using Azure Data Factory data flows
- Implementing data transformation activities and transformations
- Integrating Azure Databricks for advanced data processing
- Using Azure Data Factory mapping data flows for complex transformations
Module 5: Data Movement and Copy Activities
- Configuring data movement activities in Azure Data Factory
- Implementing data copy operations between various data stores
- Managing data integration and synchronization scenarios
- Implementing incremental data loading strategies
Module 6: Data Transformation and Integration with Mapping Data Flows
- Building and configuring mapping data flows in Azure Data Factory
- Implementing data transformations, aggregations, and filtering
- Handling complex data integration scenarios
- Performance optimization and data flow debugging
Module 7: Data Orchestration and Workflow Automation
- Configuring data pipelines and activities for workflow automation
- Implementing control flow activities and dependencies
- Using variables, expressions, and functions in data pipelines
- Scheduling and triggering data factory pipelines
Module 8: Data Monitoring and Management
- Monitoring and troubleshooting data pipelines and activities
- Implementing logging and alerting in Azure Data Factory
- Managing data factory resources and security
- Optimizing and scaling data integration and processing
Module 9: Integration with Azure Services
- Integrating Azure Data Factory with Azure Storage services
- Loading data into Azure SQL Database and Azure Synapse Analytics
- Using Azure Data Lake Storage for big data processing
- Implementing hybrid data integration scenarios
Module 10: Data Governance and Security
- Implementing data governance and data quality practices
- Securing data pipelines and activities in Azure Data Factory
- Monitoring and auditing data integration and processing
- Implementing data retention and compliance policies