Explores the design of a general-purpose distributed execution system, covering challenges, specialized frameworks, decentralized control logic, and high-performance shuffle.
Explores event time vs. processing time, stream processing operations, stream-stream joins, and handling late/out-of-order data in data stream processing.
Covers data stream processing concepts, focusing on Apache Kafka and Spark Streaming integration, event time management, and project implementation guidelines.
Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.
Covers the adaptation of analytics systems to modern hardware and data challenges, focusing on efficiency and scalability through innovative approaches and hybrid systems.
