AI Data Engineering on Azure Training Course

 AI Data Engineering on Azure Training Course 

Introduction 

AI Data Engineering on Azure Training Course provides comprehensive practical knowledge and technical expertise in designing, developing, deploying, and managing intelligent data engineering solutions using Microsoft Azure cloud technologies. The course explores modern data engineering architectures, artificial intelligence integration, cloud data platforms, big data processing, data ingestion, data transformation, data orchestration, and enterprise data management. Participants develop practical skills in Microsoft Fabric, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage, Microsoft Purview, and Azure Machine Learning. Emphasis is placed on building scalable data pipelines, implementing lakehouse architectures, developing real-time analytics solutions, and preparing high-quality datasets for generative artificial intelligence, machine learning, and advanced business intelligence applications. 

The training also examines emerging trends in AI-powered data engineering, automated data pipelines, intelligent ETL and ELT processes, vector databases, retrieval-augmented generation, data governance, and responsible artificial intelligence. Through practical exercises and international case studies, participants learn how to integrate structured and unstructured data, optimize cloud computing resources, implement secure data architectures, and support enterprise artificial intelligence initiatives. The course equips professionals with industry-relevant competencies for digital transformation, predictive analytics, intelligent automation, and data-driven decision-making across different sectors. 

Course Objectives 

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

1. Design scalable AI-driven data engineering architectures on Microsoft Azure.
2. Develop automated ETL and ELT pipelines for enterprise data integration.
3. Implement Azure Data Factory and Microsoft Fabric data workflows.
4. Build cloud-native data lakes and lakehouse architectures.
5. Integrate artificial intelligence and machine learning into data pipelines.
6. Apply Azure Databricks and Apache Spark for big data processing.
7. Develop real-time data ingestion and streaming analytics solutions.
8. Implement data quality, metadata management, and data governance.
9. Integrate vector databases and retrieval-augmented generation workflows.
10. Optimize cloud data storage, processing, and computing costs.
11. Apply data security, identity management, and access controls.
12. Implement automated data orchestration and monitoring.
13. Deploy production-ready AI data engineering solutions.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced operational costs through automated data engineering.
3. Enhanced artificial intelligence adoption and innovation.
4. Improved data quality, consistency, and reliability.
5. Faster business intelligence and reporting processes.
6. Better predictive analytics and strategic decision-making.
7. Strengthened cloud data security and regulatory compliance.
8. Increased scalability of enterprise data infrastructure.
9. Improved collaboration between data and AI teams.
10. Accelerated digital transformation and competitive advantage.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Artificial intelligence and machine learning engineers.
3. Azure cloud administrators and solutions architects.
4. Data scientists and analytics professionals.
5. Database administrators and developers.
6. Business intelligence and data platform specialists.
7. IT managers and digital transformation consultants.
8. Software engineers and enterprise technology professionals.

Course Duration: 5 days

Course Modules

Module 1: Foundations of AI Data Engineering on Azure
 

• Introduction to cloud data engineering and AI integration.
• Microsoft Azure data architecture and service ecosystem.
• Data engineering lifecycle and modern enterprise architectures.
• Structured, semi-structured, and unstructured data management.
• Data lake, data warehouse, and lakehouse concepts.
• Case study: Microsoft's enterprise cloud data architecture.

Module 2: Azure Data Ingestion and Pipeline Development
 

• Data ingestion using Azure Data Factory.
• Building automated ETL and ELT pipelines.
• Connecting APIs, databases, and cloud storage.
• Data transformation and pipeline orchestration.
• Incremental loading and change data capture.
• Case study: Global retail data integration using Azure Data Factory.

Module 3: Azure Data Lake and Lakehouse Architecture
 

• Implementing Azure Data Lake Storage Gen2.
• Designing scalable cloud storage architectures.
• Delta Lake and open data table formats.
• Data partitioning, indexing, and storage optimization.
• Managing data lifecycle and retention policies.
• Case study: Enterprise lakehouse implementation in financial services.

Module 4: Big Data Processing with Azure Databricks
 

• Introduction to Apache Spark and distributed computing.
• Developing PySpark data transformation workflows.
• Batch processing and large-scale data analytics.
• Implementing Delta Live Tables and Lakeflow concepts.
• Performance optimization and cluster management.
• Case study: Global logistics analytics using Azure Databricks.

Module 5: Microsoft Fabric and Intelligent Data Engineering
 

• Microsoft Fabric architecture and OneLake integration.
• Developing data pipelines and dataflows.
• Working with Fabric Lakehouse and Warehouse.
• Implementing notebooks and Spark-based processing.
• Integrating Power BI and enterprise analytics.
• Case study: Enterprise analytics modernization using Microsoft Fabric.

Module 6: AI Integration, Machine Learning and Generative AI
 

• Integrating Azure Machine Learning into data workflows.
• Preparing datasets for artificial intelligence applications.
• Feature engineering and automated data preparation.
• Implementing vector embeddings and vector search.
• Developing retrieval-augmented generation data pipelines.
• Case study: Azure-based intelligent customer support using enterprise knowledge data.

Module 7: Real-Time Data Engineering and Advanced Analytics
 

• Real-time data ingestion and event-driven architectures.
• Implementing Azure Event Hubs and Stream Analytics.
• Developing streaming data transformation workflows.
• Integrating real-time analytics with AI applications.
• Monitoring streaming pipelines and processing latency.
• Case study: Real-time financial transaction monitoring using Azure technologies.

Module 8: Data Governance, Security and Production Deployment
 

• Implementing Microsoft Purview and data governance.
• Managing Azure identities, permissions, and access controls.
• Data quality validation and pipeline observability.
• CI/CD, automated testing, and deployment strategies.
• Cost management, performance monitoring, and optimization.
• Case study: Secure enterprise AI data platform implementation in healthcare.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Microsoft Azure services.
• Hands-on laboratory exercises and guided projects.
• Real-world international case studies and industry applications.
• Group assignments, collaborative problem-solving, and technical workshops.
• Practical assessments, project presentations, and knowledge evaluation.

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.