Design Real-Time Architectures with Spark & Kafka

“Design Real-Time Architectures with Apache Spark & Kafka” is an intermediate-level course crafted for learners aiming to build modern, scalable streaming systems. Across engaging, scenario-driven lessons, the course offers a comprehensive introduction to designing and implementing real-time data pipelines. Participants explore the foundations of streaming concepts, event-driven patterns, and the unique demands of low-latency processing. They gain practical experience working with Apache Kafka for event ingestion and Apache Spark Structured Streaming for real-time computation, learning to transform raw streams into actionable insights. The curriculum emphasizes reliable pipeline design, covering fault tolerance, checkpointing, and performance tuning to ensure systems can operate at scale. Through hands-on practice, guided dialogues, and real-world financial data scenarios, learners develop the confidence to architect, optimize, and deploy production-ready streaming solutions. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments. Learners should know basic Python or Scala, be comfortable with the command line, understand distributed systems at a high level, and have a simple introductory familiarity with Kafka and Spark. This course is ideal for aspiring data engineers, analysts or data scientists shifting into real-time systems, and software engineers exploring event-driven architecture. It also suits anyone working with large-scale data or financial and AI/ML pipelines who wants to understand how real-time data powers modern systems. By the end of the course, they are equipped with the technical and strategic skills needed to excel in today’s data-driven, real-time environments

• Examine core real-time data principles and Kafka-Spark streaming support
• Create real-time data pipelines connecting Kafka with Spark Structured Streaming
• Improve and deploy streaming systems via monitoring, fault tolerance, and tuning

• Basic familiarity with relevant terminology and concepts
• Willingness to engage in applied exercises and case studies