Process event streams with Kafka and compare real-time transformations using Spark Streaming and Flink. 1. Who can join Suitable for: Data engineers, platform developers and distributed-systems learners Mandatory prerequisites: Python or Java, SQL, Docker and basic distributed-data concepts 2. Duration 56 live hours • Approximately 10 weeks at 6 hours per week; actual timetable to be confirmed. 3. Mode Classroom - Thaiyur / Kelambakkam, OMR Chennai (subject to schedule) Live Online (subject to schedule)
4. Syllabus — five modules
Module 1: Event-Driven Architecture: Batch vs. Real-Time Stream Processing Learning outcome: Explain event streams, latency and delivery guarantees. Module 2: Apache Kafka Fundamentals: Producers, Consumers, Topics & Schema Registry Learning outcome: Create Kafka producers, consumers and topics. Module 3: Real-Time Stream Analytics using Structured Spark Streaming Learning outcome: Transform streaming records using Spark Structured Streaming. Module 4: Stateful Event Processing with Apache Flink Learning outcome: Demonstrate stateful processing and windows using Flink. Module 5: End-to-End Stream Ingestion into Real-Time Dashboards ; Storage Learning outcome: Deliver an end-to-end alert stream and evaluate failure recovery. 5. Practical project Source capstone title: Build a Real-Time Financial Fraud Detection Pipeline using Kafka & Spark Student deliverable: A live synthetic fraud-event pipeline publishing events to Kafka, processing flags and displaying aggregate alerts in a demonstration dashboard.
6. What is included Live trainer-led sessions Practical assignments Sample code or templates, as applicable Project feedback Learning assessment Perizona course-completion certificate subject to assessment 7. Upcoming batch Start date: To be announced. Students can register interest; dates are shared only after confirmation.
