Azure Stream Analytics and Real-Time Data Training Course

  Azure Stream Analytics and Real-Time Data Training Course 

Introduction 

Azure Stream Analytics and Real-Time Data Training Course is designed to equip participants with practical knowledge and technical skills for developing, managing, and optimizing real-time data processing solutions using Microsoft Azure. The course explores modern streaming data architecture, event-driven computing, cloud-based data integration, and real-time analytics using Azure Stream Analytics, Azure Event Hubs, Azure IoT Hub, Azure Data Lake, and Microsoft Power BI. Participants will learn how to ingest, process, transform, analyze, and visualize high-velocity data streams to support data-driven decision-making, operational intelligence, and business automation. Emphasis is placed on scalable cloud infrastructure, low-latency processing, data quality, fault tolerance, and advanced streaming analytics. 

Through practical exercises and global industry case studies, participants will develop expertise in designing real-time data pipelines, writing Stream Analytics Query Language (SAQL) queries, implementing windowing functions, managing event-time processing, and integrating streaming workloads with downstream storage and visualization platforms. The training also examines applications in financial services, telecommunications, healthcare, manufacturing, transportation, and smart cities. Participants will gain practical experience in monitoring streaming applications, optimizing performance, implementing security controls, and deploying enterprise-grade real-time analytics solutions that improve operational efficiency, predictive capabilities, and organizational competitiveness. 

Course Objectives 

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

1. Understand Azure Stream Analytics architecture and real-time data processing.
2. Design scalable cloud-based streaming data pipelines.
3. Configure Azure Event Hubs and Azure IoT Hub.
4. Develop advanced Stream Analytics Query Language queries.
5. Implement real-time data ingestion and transformation.
6. Apply tumbling, hopping, sliding, and session windowing functions.
7. Integrate Azure Stream Analytics with Power BI dashboards.
8. Implement event-driven architecture and cloud data integration.
9. Optimize streaming performance and processing latency.
10. Apply data security, governance, and access management.
11. Monitor streaming jobs and troubleshoot processing errors.
12. Develop real-time analytics solutions for enterprise applications.
13. Implement fault tolerance and scalable streaming infrastructure.

Organizational Benefits
 

1. Improved real-time business intelligence and decision-making.
2. Faster identification of operational risks and anomalies.
3. Enhanced enterprise data integration and accessibility.
4. Reduced data processing delays and operational costs.
5. Improved predictive analytics and performance monitoring.
6. Increased automation of business processes.
7. Better customer experience through timely insights.
8. Enhanced cloud infrastructure scalability.
9. Stronger data security and governance.
10. Improved organizational competitiveness and innovation.

Target Audiences
 

1. Data engineers and analytics professionals.
2. Cloud architects and Azure administrators.
3. Business intelligence and data visualization specialists.
4. Software developers and application engineers.
5. Internet of Things (IoT) specialists.
6. Database administrators and data scientists.
7. IT managers and enterprise technology consultants.
8. Professionals responsible for digital transformation.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Azure Stream Analytics
 

• Real-time analytics architecture and streaming data fundamentals.
• Azure Stream Analytics components and cloud deployment models.
• Batch processing versus real-time data processing.
• Streaming applications and enterprise use cases.
• Data ingestion, processing, and output architecture.
• Case study: Smart city traffic monitoring and real-time congestion analytics in Singapore.

Module 2: Azure Streaming Data Ingestion and Integration
 

• Configuring Azure Event Hubs and event producers.
• Integrating Azure IoT Hub for device telemetry.
• Managing event streams and data ingestion pipelines.
• Connecting streaming sources with Azure cloud services.
• Data serialization formats, including JSON, CSV, and Avro.
• Case study: Industrial IoT sensor data collection and monitoring in Germany.

Module 3: Stream Analytics Query Language and Data Transformation
 

• Introduction to Stream Analytics Query Language syntax.
• Filtering, projecting, and transforming streaming records.
• Implementing aggregations and calculated fields.
• Handling timestamps, null values, and malformed records.
• Joining streaming data with reference datasets.
• Case study: Real-time financial transaction processing and fraud detection in the United Kingdom.

Module 4: Windowing Functions and Event-Time Processing
 

• Understanding tumbling and hopping windows.
• Implementing sliding and session window functions.
• Managing event-time and arrival-time processing.
• Handling late-arriving and out-of-order events.
• Applying temporal aggregation and stream correlation.
• Case study: Real-time public transportation tracking and passenger flow analysis in London.

Module 5: Real-Time Analytics and Power BI Integration
 

• Connecting Azure Stream Analytics outputs to Power BI.
• Developing real-time dashboards and interactive reports.
• Implementing streaming datasets and visualization.
• Creating operational performance indicators and alerts.
• Integrating analytical outputs with business intelligence systems.
• Case study: Real-time retail sales monitoring and inventory optimization in the United States.

Module 6: Streaming Data Storage and Downstream Integration
 

• Configuring Azure Blob Storage and Azure Data Lake Storage.
• Integrating streaming outputs with Azure SQL Database.
• Managing historical and real-time data storage.
• Designing scalable data lake ingestion architectures.
• Implementing data retention and archival strategies.
• Case study: Healthcare patient monitoring and real-time medical data integration in Australia.

Module 7: Monitoring, Security, and Performance Optimization
 

• Monitoring Azure Stream Analytics jobs and resource utilization.
• Implementing Azure role-based access control and identity management.
• Configuring encryption and secure data connections.
• Troubleshooting streaming errors and processing bottlenecks.
• Optimizing throughput, latency, and query performance.
• Case study: Telecommunications network performance monitoring and anomaly detection in South Korea.

Module 8: Enterprise Deployment and Real-Time Analytics Projects
 

• Designing production-ready streaming analytics solutions.
• Configuring job deployment and resource scaling.
• Implementing fault tolerance and recovery strategies.
• Developing end-to-end real-time data processing projects.
• Applying cloud governance and operational best practices.
• Case study: Manufacturing equipment predictive maintenance and automated production monitoring in Japan.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Microsoft Azure services.
• Hands-on laboratory exercises and guided configurations.
• Real-world streaming data simulations and query development.
• International industry case studies and group assignments.
• Individual and collaborative real-time analytics projects.
• Practical assessments, troubleshooting exercises, and knowledge evaluations.

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.