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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Community, affiliation, and strengths

Let's now look at the network connections and others. These algorithms are applied widely for fraud detection and security applications. Triangular spamming is a well-known technique that can be detected using the triangle count and community algorithms. Another interesting application of the triangle count is to estimate and rank communities. The age of a community is related to the density of the triangles; new communities will have fewer triangles, and as the communities mature, triangles start to form. Another interesting application is the concept of a heavy hitter in a community, defined as any vertex that has more than sqrt(n) degrees. Finding heavy hitter triangles would be like finding influential people in a community. Connected communities and strongly connected communities expose the structure in an underlying graph, akin to the Panama papers. And all these are APIs in GraphX. No wonder GraphX is part of the processing stack for Linkedin...

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