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You're reading from  Clojure for Data Science

Product typeBook
Published inSep 2015
Reading LevelIntermediate
Publisher
ISBN-139781784397180
Edition1st Edition
Languages
Clojure
Concepts
Data Analysis
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Author (1)
Henry Garner
Henry Garner
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Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner

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Summary

In this chapter, we've learned about graphs: a useful abstraction to model a huge variety of phenomena. We started the chapter using the Clojure library Loom to visualize and traverse small graphs of Twitter followers. We learned about two different methods of graph traversal, depth-first and breadth-first search, and the effect of changing edge weights on the paths discovered by Dijkstra's algorithm and Prim's algorithm. We also looked at the density of the whole graph and plotted the degree distributions to observe the difference between random and scale-free graphs.

We introduced GraphX and the Clojure library Glittering as a means of processing large graphs in a scalable way using Spark. In addition to providing several built-in graph algorithms, Glittering also exposes GraphX's Pregel API: a set of three symbiotic functions to express graph algorithms in a vertex-centric way. We showed that this alternative model of computation could be used to express triangle counting, connected...

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Clojure for Data Science
Published in: Sep 2015Publisher: ISBN-13: 9781784397180
© 2015 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner

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