Advanced Azure Analytics Engineering Training Course

 Advanced Azure Analytics Engineering Training Course 

Introduction 

Advanced Azure Analytics Engineering Training Course is designed to equip professionals with advanced technical expertise in Microsoft Azure analytics, cloud data engineering, enterprise data architecture, and business intelligence. The course provides practical knowledge of Azure Synapse Analytics, Microsoft Fabric, Azure Data Factory, Azure Databricks, Azure Data Lake Storage, and Power BI to support modern data management and analytics environments. Participants explore cloud-native data platforms, scalable data pipelines, data lakehouse architecture, big data processing, data transformation, and advanced analytics engineering techniques. Emphasis is placed on building secure, scalable, automated, and cost-effective enterprise analytics solutions that support data-driven decision-making. 

The training also explores modern data engineering practices, including DataOps, analytics automation, real-time data streaming, machine learning integration, data governance, and enterprise data quality management. Participants gain practical experience designing end-to-end analytical workflows, optimizing distributed data processing, implementing data integration strategies, and developing reliable data models for enterprise reporting. Through international case studies and hands-on exercises, learners examine how organizations leverage Azure cloud technologies to modernize analytics infrastructure, improve operational efficiency, and achieve digital transformation objectives. 

Course Objectives 

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

1. Design advanced Azure analytics engineering architectures.
2. Develop scalable cloud-based data engineering solutions.
3. Implement enterprise data lakehouse architectures.
4. Configure Azure Synapse Analytics environments.
5. Build automated pipelines using Azure Data Factory.
6. Perform advanced data transformation using Azure Databricks.
7. Integrate Microsoft Fabric into enterprise analytics.
8. Implement real-time data streaming and processing.
9. Develop optimized analytical data models.
10. Apply DataOps and analytics automation practices.
11. Implement enterprise data governance and security.
12. Optimize cloud analytics performance and costs.
13. Develop advanced business intelligence solutions.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced analytics infrastructure and operational costs.
3. Enhanced data governance and regulatory compliance.
4. Faster business intelligence and reporting processes.
5. Improved data quality and consistency.
6. Increased operational efficiency through automation.
7. Better strategic decision-making using advanced analytics.
8. Scalable cloud-based data infrastructure.
9. Improved collaboration between data engineering teams.
10. Accelerated enterprise digital transformation.

Target Audiences
 

1. Azure Data Engineers and Cloud Engineers.
2. Analytics Engineers and Data Architects.
3. Business Intelligence Developers.
4. Data Scientists and Machine Learning Engineers.
5. Database Administrators.
6. Enterprise IT Managers and Consultants.
7. Cloud Solutions Architects.
8. Data Governance and Analytics Professionals.

Course Duration: 10 days

Course Modules

Module 1: Advanced Azure Analytics Engineering Architecture
 

• Azure analytics ecosystem and enterprise architecture.
• Cloud-native analytics infrastructure design.
• Distributed data processing frameworks.
• Enterprise analytics integration strategies.
• Scalability, availability, and performance engineering.
• Case study: Microsoft enterprise cloud analytics architecture.

Module 2: Azure Synapse Analytics Engineering
 

• Synapse workspace architecture and configuration.
• Dedicated and serverless SQL pools.
• Apache Spark integration and analytics processing.
• Advanced SQL analytics and query optimization.
• Synapse security and workload management.
• Case study: Retail analytics modernization using Azure Synapse.

Module 3: Azure Data Factory and Pipeline Engineering
 

• Advanced pipeline orchestration and automation.
• Data integration and transformation workflows.
• Parameterization and reusable pipeline development.
• Incremental loading and change data capture.
• Pipeline monitoring and failure management.
• Case study: Global banking data integration using Azure Data Factory.

Module 4: Azure Data Lake and Lakehouse Architecture
 

• Azure Data Lake Storage Gen2 architecture.
• Delta Lake and lakehouse implementation.
• Data partitioning and storage optimization.
• Medallion architecture and data organization.
• Enterprise data lifecycle management.
• Case study: Global retail lakehouse implementation.

Module 5: Advanced Azure Databricks Engineering
 

• Apache Spark architecture and distributed computing.
• PySpark transformations and optimization.
• Delta Lake processing and management.
• Databricks workflows and job automation.
• Performance tuning and cluster management.
• Case study: Netflix-scale streaming analytics architecture.

Module 6: Microsoft Fabric and Unified Analytics
 

• Microsoft Fabric architecture and capabilities.
• OneLake storage and data integration.
• Fabric Data Factory and engineering workloads.
• Lakehouse and warehouse implementation.
• Power BI integration and semantic models.
• Case study: Enterprise analytics modernization using Microsoft Fabric.

Module 7: Advanced SQL and Analytics Data Modeling
 

• Advanced SQL development and optimization.
• Dimensional modeling and star schemas.
• Data warehouse design and implementation.
• Slowly changing dimensions and historical data.
• Semantic modeling and analytical performance.
• Case study: Global financial services data warehouse development.

Module 8: Real-Time Analytics and Data Streaming
 

• Azure Event Hubs architecture and configuration.
• Azure Stream Analytics implementation.
• Real-time data ingestion and transformation.
• Streaming analytics and event-driven processing.
• Low-latency analytics architecture.
• Case study: Smart city real-time transportation analytics.

Module 9: Advanced DataOps and Analytics Automation
 

• DataOps principles and implementation.
• CI/CD for Azure analytics engineering.
• Git integration and version control.
• Automated testing and deployment pipelines.
• Infrastructure as Code and workflow orchestration.
• Case study: Enterprise analytics automation in multinational organizations.

Module 10: Data Governance, Security and Compliance
 

• Microsoft Purview and data cataloguing.
• Data lineage and metadata management.
• Role-based access control and identity management.
• Data privacy and regulatory compliance.
• Enterprise data quality frameworks.
• Case study: Financial institution enterprise data governance.

Module 11: Advanced Power BI and Enterprise Reporting
 

• Advanced Power BI architecture and connectivity.
• DAX calculations and analytical expressions.
• Enterprise semantic models and reporting.
• Dashboard performance optimization.
• Automated reporting and business intelligence.
• Case study: Global retail business intelligence transformation.

Module 12: Machine Learning and Predictive Analytics Integration
 

• Azure Machine Learning integration.
• Predictive analytics and forecasting.
• Feature engineering and data preparation.
• Machine learning pipeline automation.
• Operationalizing analytical models.
• Case study: Predictive maintenance in manufacturing.

Module 13: Data Quality and Analytics Performance Optimization
 

• Automated data validation techniques.
• Data profiling and anomaly detection.
• Query and workload optimization.
• Distributed processing performance tuning.
• Analytics monitoring and troubleshooting.
• Case study: Enterprise data quality improvement in telecommunications.

Module 14: Enterprise Analytics Engineering and Cost Management
 

• Multi-environment analytics architecture.
• Azure resource allocation and optimization.
• Cloud cost monitoring and FinOps practices.
• High availability and disaster recovery.
• Enterprise workload management.
• Case study: Global enterprise cloud analytics cost optimization.

Module 15: Advanced Analytics Engineering Capstone Project
 

• Design an end-to-end Azure analytics architecture.
• Develop integrated enterprise data pipelines.
• Implement lakehouse and warehouse solutions.
• Build automated analytics and reporting workflows.
• Evaluate security, scalability, and performance.
• Case study: International enterprise digital transformation using Azure analytics technologies.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical Azure cloud laboratory exercises.
• Hands-on analytics engineering projects.
• Real-world international case studies.
• Collaborative group discussions and problem-solving.
• Guided pipeline development and deployment.
• Practical assessments and capstone project presentations.

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.