Big Data Challenges: Scaling to Massive Data

This lecture by the instructor covers the challenges of handling massive data in the era of big data. Starting with the limitations of single-machine data processing, the lecture delves into the growing data sources and the need for distributed computing. It explores examples of large datasets from companies like Facebook and Google, emphasizing the necessity of distributed data storage and processing. The lecture discusses the power law distribution of data and the hardware requirements for big data processing. It introduces concepts like MapReduce and Spark as solutions for distributed data processing, highlighting their architecture and key features. The lecture also touches on concepts like lazy execution, transformations, actions, broadcast variables, accumulators, RDD persistence, and Spark DataFrames.