Azure Data Factory and Data Integration Training Course

 Azure Data Factory and Data Integration Training Course 

Introduction 

Azure Data Factory and Data Integration Training Course is designed to equip participants with practical skills in modern cloud data engineering, enterprise data integration, and automated data pipeline development using Microsoft Azure. The course provides comprehensive knowledge of Azure Data Factory, cloud-based Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes, data orchestration, data transformation, and workflow automation. Participants will explore how to design scalable data integration architectures, connect heterogeneous data sources, implement metadata-driven pipelines, and manage structured and unstructured datasets across cloud and on-premises environments. The training incorporates modern data engineering practices, Microsoft Azure cloud technologies, data governance, and enterprise data management strategies. 

Participants will gain hands-on experience developing, scheduling, monitoring, and troubleshooting data pipelines while integrating Azure Data Factory with Azure Data Lake Storage, Azure SQL Database, Azure Synapse Analytics, and other enterprise platforms. Through practical exercises and global industry case studies, participants will learn to implement secure data transfer, optimize pipeline performance, manage data dependencies, and establish reliable data integration workflows. The course also explores advanced capabilities such as parameterization, incremental data loading, CI/CD deployment, event-driven automation, and cloud data governance to support digital transformation, business intelligence, advanced analytics, and artificial intelligence initiatives. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand Azure Data Factory architecture and cloud data engineering principles.
2. Develop scalable ETL and ELT data integration pipelines.
3. Implement automated data ingestion and orchestration workflows.
4. Integrate cloud, hybrid, and on-premises data sources.
5. Apply advanced data transformation and mapping techniques.
6. Configure Azure Integration Runtime and Self-hosted Integration Runtime.
7. Implement incremental loading and change data capture.
8. Apply metadata-driven pipeline development techniques.
9. Configure secure data access and identity management.
10. Monitor, troubleshoot, and optimize pipeline performance.
11. Integrate Azure Data Factory with Azure Synapse Analytics.
12. Implement continuous integration and continuous delivery (CI/CD).
13. Apply enterprise data governance and cloud data management practices.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced manual data processing and operational costs.
3. Enhanced data pipeline automation and productivity.
4. Improved business intelligence and reporting capabilities.
5. Scalable cloud data engineering infrastructure.
6. Stronger data security and governance frameworks.
7. Faster data availability for business decision-making.
8. Improved operational efficiency across departments.
9. Better integration of hybrid and cloud environments.
10. Increased organizational readiness for artificial intelligence and advanced analytics.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Database administrators and developers.
3. Business intelligence professionals.
4. Data analysts and analytics specialists.
5. Azure cloud administrators and engineers.
6. ETL and data integration developers.
7. Information technology project managers.
8. Enterprise data management professionals.

Course Duration: 10 days

Course Modules

Module 1: Introduction to Azure Data Factory
 

• Cloud data integration fundamentals.
• Azure Data Factory architecture and components.
• Data pipelines, activities, datasets, and linked services.
• Azure Portal navigation and workspace configuration.
• Global case study: Enterprise cloud data integration at Microsoft.

Module 2: Azure Data Factory Architecture and Setup
 

• Creating and configuring Azure Data Factory instances.
• Understanding Azure subscriptions and resource groups.
• Configuring integration runtimes.
• Managing triggers and pipeline execution.
• Global case study: Hybrid data infrastructure implementation in a multinational enterprise.

Module 3: Data Sources and Connectivity
 

• Connecting Azure SQL Database and SQL Server.
• Integrating Azure Blob Storage and Data Lake Storage.
• Connecting REST APIs and external data sources.
• Configuring linked services and datasets.
• Global case study: Retail data integration across multiple international branches.

Module 4: ETL and ELT Pipeline Development
 

• Designing ETL and ELT architectures.
• Creating pipeline activities and dependencies.
• Implementing Copy Data activities.
• Configuring data ingestion workflows.
• Global case study: Automated financial data processing in global banking.

Module 5: Data Transformation and Mapping
 

• Introduction to Mapping Data Flows.
• Data cleansing and standardization.
• Implementing joins, filters, and aggregations.
• Schema mapping and transformation logic.
• Global case study: Healthcare data consolidation across hospital networks.

Module 6: Advanced Pipeline Development
 

• Pipeline parameterization and dynamic expressions.
• Variables, parameters, and reusable components.
• Conditional execution and looping activities.
• Error handling and retry mechanisms.
• Global case study: Automated supply chain data processing.

Module 7: Data Orchestration and Scheduling
 

• Pipeline orchestration techniques.
• Schedule, tumbling window, and event-based triggers.
• Managing pipeline dependencies.
• Event-driven data processing.
• Global case study: Real-time logistics data synchronization.

Module 8: Incremental Data Loading and Change Data Capture
 

• Incremental loading architecture.
• Watermark-based data ingestion.
• Change Data Capture fundamentals.
• Managing historical and transactional data.
• Global case study: Incremental customer data synchronization in banking.

Module 9: Azure Data Lake Integration
 

• Azure Data Lake Storage architecture.
• Integrating hierarchical namespace storage.
• Managing structured and unstructured datasets.
• Implementing data lake ingestion pipelines.
• Global case study: Large-scale enterprise data lake implementation.

Module 10: Azure Synapse Analytics Integration
 

• Azure Synapse Analytics architecture.
• Connecting Azure Data Factory to Synapse.
• Implementing analytical data pipelines.
• Loading and transforming warehouse datasets.
• Global case study: Enterprise business intelligence modernization.

Module 11: Security and Data Governance
 

• Azure Role-Based Access Control.
• Managed identities and authentication.
• Azure Key Vault integration.
• Data access policies and secure credentials.
• Global case study: Secure financial data integration in regulated industries.

Module 12: Pipeline Monitoring and Troubleshooting
 

• Monitoring pipeline execution.
• Analyzing activity runs and execution logs.
• Identifying and resolving pipeline failures.
• Performance optimization and resource management.
• Global case study: Enterprise pipeline reliability improvement.

Module 13: CI/CD and Deployment Management
 

• Azure DevOps integration.
• Git-based development workflows.
• Automated pipeline deployment.
• Environment configuration and release management.
• Global case study: Continuous deployment of data engineering solutions.

Module 14: Advanced Data Integration and Automation
 

• REST API integration and pagination.
• Metadata-driven pipeline architecture.
• Azure Functions integration.
• Automation using event-driven workflows.
• Global case study: Automated global e-commerce data integration.

Module 15: Enterprise Data Integration Project
 

• Designing an end-to-end integration architecture.
• Developing automated data ingestion pipelines.
• Implementing transformations and orchestration.
• Testing, monitoring, and deployment.
• Global case study: Enterprise-wide cloud data integration for a multinational organization.

Training Methodology
 

• Instructor-led presentations and interactive discussions.
• Practical demonstrations using Microsoft Azure services.
• Hands-on laboratory exercises and pipeline development.
• Real-world enterprise scenarios and international case studies.
• Group projects, technical assessments, and knowledge-sharing sessions.
• Practical troubleshooting exercises and project-based learning.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.