January 2016
Beginner to intermediate
304 pages
8h 16m
English
book
Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing
by Mohammed Guller
Content preview from Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large-Scale Data Processing, Machine Learning, and Graph Analytics, and High-Velocity Data Stream Processing
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CHAPTER 9
Graph Processing with Spark
Data is generally stored and processed as a collection of records or rows. It is represented as a two-dimensional table with data divided into rows and columns. However, collections or tables are not the only way to represent data. Sometimes, a graph provides a better representation of data than a collection.
Graphs are ubiquitous. They are everywhere around us. For example, the Internet is a large graph of interconnected computers, routers, and switches. The World Wide Web is a large graph. Web pages connected by hypertext links form a graph. Social networks on sites such as Facebook, LinkedIn, and Twitter ...
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