Spark Streaming: Fault Tolerance and DStreams

This lecture covers Spark Streaming, which enables real-time analysis of big data by processing data as soon as it arrives. It discusses fault tolerance techniques for streaming platforms, including replication and upstream backup. The concept of DStreams, a sequence of immutable, partitioned datasets, is explained. Examples of streaming word count and sliding window operations are provided, showcasing the inter-mixing of RDD and DStream operations. The lecture also explores fault-tolerance mechanisms such as RDD lineage and fast fault recovery within Spark Streaming, aiming to unify batch and stream processing models.