This lecture covers the design choices of Big Data systems, focusing on storage layer, programming model, execution engine, resource management, and fault tolerance. It explains how systems like Yarn enable multiple frameworks to co-exist, decisions of varying granularities in resource management, architectural choices of Spark, and the importance of fault tolerance in the face of hardware/software failures. The lecture also discusses data safety, job recovery in Spark, and the impact of failures on performance.
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