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