Lecture

Dataflow: Execution Models for Distributed Computing

Description

This lecture covers the data flow model, focusing on improving expressiveness, extensibility, and performance in distributed computing. It explains Resilient Distributed Datasets (RDDs) in Spark, their properties, operations, and examples like error finding and word count. The lecture also discusses the limitations of vanilla Spark and the benefits of lazy evaluation.

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