Microsoft Azure Data Engineering Training Course

 Microsoft Azure Data Engineering Training Course 

Introduction 

Microsoft Azure Data Engineering Training Course is designed to equip participants with practical skills and advanced knowledge in designing, developing, implementing, and managing modern cloud-based data engineering solutions using Microsoft Azure. The course explores essential Azure data services, including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, Azure Databricks, Azure Stream Analytics, and Microsoft Fabric integration. Participants will gain expertise in data ingestion, ETL and ELT pipelines, big data processing, data transformation, data warehousing, data lake architecture, and cloud data integration. Emphasis is placed on developing scalable, secure, cost-effective, and high-performance data platforms that support enterprise analytics, business intelligence, and data-driven decision-making. 

The training further explores modern data engineering practices, including Apache Spark processing, Python-based data transformation, SQL development, real-time data streaming, data orchestration, data governance, metadata management, and automated pipeline deployment. Through practical exercises and global industry case studies, participants will learn how organizations leverage Microsoft Azure to modernize data infrastructure, optimize analytical workloads, and implement reliable enterprise data solutions. The course also covers cloud security, identity management, monitoring, disaster recovery, and performance optimization, preparing professionals to build production-ready data engineering environments aligned with contemporary cloud computing and big data industry standards. 

Course Objectives 

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

1. Design scalable Microsoft Azure data engineering architectures.
2. Develop automated ETL and ELT data pipelines.
3. Implement Azure Data Factory orchestration solutions.
4. Manage cloud-based data lakes and enterprise storage.
5. Apply Apache Spark and Python for big data processing.
6. Develop advanced SQL queries and data transformations.
7. Implement Azure Synapse Analytics solutions.
8. Integrate batch and real-time data streaming.
9. Apply data governance, security, and compliance frameworks.
10. Optimize cloud data workloads and processing performance.
11. Implement automated data pipeline monitoring.
12. Apply DevOps and continuous integration practices.
13. Develop secure, reliable, and cost-efficient data platforms.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced infrastructure and operational costs.
3. Enhanced business intelligence and reporting capabilities.
4. Faster processing of large-scale organizational datasets.
5. Improved data security and regulatory compliance.
6. Increased automation of data management processes.
7. Better forecasting and data-driven decision-making.
8. Enhanced scalability of cloud data infrastructure.
9. Improved operational efficiency and productivity.
10. Stronger organizational capacity for digital transformation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Database administrators and SQL developers.
3. Business intelligence and analytics professionals.
4. Big data engineers and data scientists.
5. Cloud computing and IT infrastructure specialists.
6. Software developers and systems engineers.
7. Data analysts and enterprise reporting specialists.
8. IT managers and digital transformation professionals.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Microsoft Azure Data Engineering
 

• Overview of Azure cloud computing and data engineering.
• Azure data architecture and service ecosystem.
• Introduction to Azure portal and resource management.
• Cloud data storage and processing fundamentals.
• Enterprise data engineering architecture design.
• Case study: Microsoft's Azure-based enterprise data modernization.

Module 2: Azure Data Lake Storage and Data Architecture
 

• Azure Data Lake Storage Gen2 configuration.
• Data lake and lakehouse architecture.
• Storage containers, directories, and access management.
• Data partitioning and storage optimization.
• Structured, semi-structured, and unstructured data.
• Case study: Global retail data lake implementation using Azure.

Module 3: Azure Data Factory and Pipeline Development
 

• Azure Data Factory architecture and components.
• Building automated ETL and ELT pipelines.
• Data ingestion using linked services and datasets.
• Pipeline triggers, scheduling, and orchestration.
• Error handling and pipeline monitoring.
• Case study: Global logistics data integration using Azure Data Factory.

Module 4: Azure Databricks and Apache Spark
 

• Introduction to Azure Databricks architecture.
• Apache Spark processing and distributed computing.
• Python and PySpark for data transformation.
• Data cleansing and large-scale processing.
• Spark performance tuning and optimization.
• Case study: Global financial analytics using distributed Spark processing.

Module 5: Azure Synapse Analytics and Data Warehousing
 

• Azure Synapse Analytics architecture.
• Building enterprise data warehouse solutions.
• Dedicated and serverless SQL pools.
• Data modelling and dimensional warehouse design.
• Integrating data lakes with analytical workloads.
• Case study: Enterprise financial reporting using Azure Synapse Analytics.

Module 6: Real-Time Data Engineering and Stream Analytics
 

• Introduction to real-time data processing.
• Azure Event Hubs and event ingestion.
• Azure Stream Analytics query development.
• Streaming data transformation and monitoring.
• Integrating real-time and batch processing.
• Case study: Global manufacturing IoT monitoring using Azure streaming services.

Module 7: Data Governance, Security, and Quality Management
 

• Azure identity and access management.
• Role-based access control and data encryption.
• Microsoft Purview and data governance.
• Data quality validation and metadata management.
• Regulatory compliance and information protection.
• Case study: Global banking data governance and security implementation.

Module 8: Data Engineering DevOps, Monitoring, and Optimization
 

• Azure Monitor and pipeline performance monitoring.
• CI/CD implementation for data engineering.
• Infrastructure deployment and automation.
• Cost management and workload optimization.
• Backup, disaster recovery, and operational reliability.
• Case study: Global enterprise cloud data platform optimization.

Training Methodology
 

• Instructor-led presentations and interactive discussions.
• Practical demonstrations using Microsoft Azure services.
• Hands-on exercises and guided laboratory sessions.
• Real-world enterprise data engineering case studies.
• Group projects, technical assessments, and problem-solving activities.
• Practical pipeline development and performance optimization exercises.

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.