Streaming Data Architectures for Developers provides a comprehensive exploration of real-time data processing frameworks and technologies essential for modern data-driven applications. This course emphasizes practical, hands-on experience through project-based learning, enabling participants to implement and optimize streaming data solutions effectively. Participants will engage in collaborative projects that culminate in publishable results, fostering both individual growth and community knowledge sharing.
The curriculum is designed to equip learners with the skills necessary to design, develop, and deploy robust streaming data architectures. By focusing on industry-relevant tools and methodologies, participants will gain insights into the intricacies of real-time analytics, data ingestion, and processing pipelines. The course culminates in a capstone project where learners will create a fully functional streaming application, showcasing their acquired expertise in a professional context.
Introduction to Streaming Data Concepts
Overview of Streaming Data Technologies (e.g., Apache Kafka, Apache Flink)
Data Ingestion Techniques for Real-Time Analytics
Stream Processing vs. Batch Processing: Key Differences
Designing Scalable Streaming Architectures
Implementing Data Quality and Validation in Streams
Real-Time Data Integration with Cloud Services
Performance Tuning and Optimization Strategies
Case Studies: Successful Streaming Data Implementations
Final Project: Develop a Streaming Application with Real-World Use Case

Real-Time Insights for Retail and E-commerce equips participants with the analytical skills necessary to harness data for immediate decision-making in dynamic market environments. Through a project-based approach, learners will engage with real-world datasets, employing various analytical techniques to derive actionable insights that can enhance business performance. This course emphasizes hands-on experience, allowing participants to work on projects that reflect current industry challenges and trends.
The curriculum is structured to facilitate a deep understanding of data analytics tools and methodologies relevant to retail and e-commerce sectors. Participants will explore topics ranging from data collection and preprocessing to advanced predictive modeling and visualization techniques. The final project will require learners to apply their knowledge to a comprehensive case study, culminating in a presentation of their findings, which may be published in Cademix Magazine. This course not only prepares individuals for immediate application in the workplace but also fosters a community of practice among peers.
Introduction to Real-Time Data Analytics
Data Collection Techniques for Retail and E-commerce
Data Preprocessing and Cleaning Strategies
Exploratory Data Analysis (EDA) for Retail Insights
Predictive Analytics: Forecasting Sales Trends
Customer Segmentation and Behavior Analysis
Real-Time Dashboard Creation with Visualization Tools
A/B Testing and Experimentation in E-commerce
Machine Learning Algorithms for Retail Applications
Final Project: Analyzing a Retail Data Set and Presenting Insights

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