Stream Processing and Fault Tolerance

This lecture covers the concepts of stream processing and fault tolerance in big data analytics. It discusses the measurement of time in data streams, efficient stream management techniques, scaling-out platforms like Spark Streaming and Apache Flink, fault tolerance strategies such as replication and upstream backup, and the use of DStreams for discretized stream processing. The instructor explains fault tolerance techniques for stream processing systems, including state partitioning and immutable tasks. Examples of streaming word count and sliding window operations are provided, showcasing the combination of batch and streaming computations. The lecture concludes with a vision of unifying batch and stream processing models in a single stack.