Dynamic Data Processing with Azure Stream Analytics

Dynamic Data Processing with Azure Stream Analytics equips participants with the skills to analyze and process streaming data in real-time. This course emphasizes hands-on projects that allow learners to apply theoretical knowledge in practical scenarios, fostering an interactive learning environment. Participants will engage in real-world applications, enabling them to extract actionable insights from data streams and improve decision-making processes in various business contexts.
Throughout the course, learners will explore the intricacies of Azure Stream Analytics, including data ingestion, transformation, and output. The curriculum is designed to ensure participants can effectively utilize Azure's capabilities to manage and analyze large volumes of data. By the end of the program, participants will have completed a comprehensive final project that demonstrates their proficiency in dynamic data processing, which they are encouraged to publish in Cademix Magazine.
Syllabus:
Introduction to Azure Stream Analytics and its architecture
Setting up Azure Stream Analytics for real-time data processing
Data ingestion techniques from various sources (IoT devices, databases, etc.)
Stream processing concepts: windowing, filtering, and aggregation
Writing and optimizing Stream Analytics queries
Integrating Azure Functions for advanced processing scenarios
Handling data output to various destinations (Azure Blob Storage, Power BI, etc.)
Monitoring and troubleshooting Azure Stream Analytics jobs
Best practices for performance tuning and cost management
Capstone project: Developing a real-time analytics solution using Azure Stream Analytics

Equip participants with the ability to implement and manage real-time data analytics solutions using Azure Stream Analytics.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of data analytics concepts, familiarity with Azure services, and experience with SQL programming.