Stream Processing with Apache Kafka & Flink is an advanced course that teaches learners to build highly responsive, scalable, and fault-tolerant real-time data applications. The course begins by exploring the importance of stream processing in modern data architectures, where businesses require immediate insights from continuous data sources such as IoT devices, user activity logs, transactions, and telemetry. Learners first dive into Apache Kafka, a high-throughput distributed messaging system used to publish and subscribe to streams of records. They explore Kafka’s architecture, including brokers, topics, partitions, producers, consumers, and Kafka Streams API. Students practice building real-time pipelines using Kafka Connect, integrating sources like PostgreSQL and MongoDB with destinations such as Elasticsearch, S3, and HDFS. Emphasis is placed on message durability, ordering, and exactly-once semantics. The course then transitions into Apache Flink, a stream processing framework known for its native support of event time, complex windowing, and strong consistency guarantees. Learners work with Flink’s DataStream and Table APIs, implementing transformations such as map, flatMap, filter, reduce, and keyBy operations. Stateful processing is introduced through keyed states, operator states, and broadcast states. Advanced concepts like watermarks, allowed lateness, and event-time joins are covered, enabling precise handling of out-of-order and delayed data. The course also covers windowing strategies including tumbling, sliding, and session windows for real-time aggregations. Students learn fault tolerance via distributed snapshots, checkpoints, savepoints, and Flink’s recovery mechanisms. Integration with Kafka for ingestion and sinks like Cassandra, MySQL, and REST APIs is demonstrated in practical labs. Real-world use cases include fraud detection systems, anomaly detection in IoT networks, and personalized recommendation engines. Learners explore deployment patterns on Kubernetes, Apache YARN, and standalone clusters, with Flink job orchestration, high availability, and logging. They configure Flink dashboards and metric systems for real-time monitoring using Prometheus and Grafana. Performance tuning topics include task slot management, memory configuration, and checkpoint intervals. Security best practices such as SSL encryption, SASL authentication, and access control with Kafka ACLs are included to ensure secure streaming infrastructure. The course emphasizes end-to-end pipeline observability with logs, metrics, and alerting strategies. By the end, learners will have built several end-to-end real-time applications that integrate Kafka producers and consumers with complex Flink jobs capable of handling mission-critical, high-velocity data. They will be equipped to deploy and maintain real-time data infrastructures that meet the demands of industries such as fintech, e-commerce, logistics, and IoT. This course is ideal for aspiring data engineers, stream processing developers, and cloud-native architects who want to move beyond batch analytics and deliver actionable insights in real time using industry-leading technologies.