Lecture 15: Big Data: Spark

The podcast elucidates Spark, a successor to MapReduce, focusing on its architecture, execution model, and fault tolerance. Spark generalizes MapReduce's two stages into multi-step data flow graphs, enhancing flexibility and optimization. A PageRank algorithm example illustrates Spark's advantage in iterative applications, which are cumbersome in MapReduce due to its lack of native iteration support and heavy reliance on file I/O. The discussion covers Spark's use of lineage graphs for computation recipes, its handling of narrow and wide dependencies, and optimizations like caching and checkpointing. Spark's fault tolerance strategy involves recomputing lost worker data, with considerations for wide dependencies and the importance of deterministic transformations. While excelling in batch processing, Spark has limitations in stream processing, addressed by Spark Streaming.