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Clojure for Data Science

You're reading from Clojure for Data Science

Product type Book

Published in Sep 2015

Publisher

ISBN-13 9781784397180

Pages 608 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Henry Garner

Table of Contents (18) Chapters

Clojure for Data Science

Credits

About the Author

Acknowledgments

About the Reviewer

www.PacktPub.com

Preface

1. Statistics

2. Inference

3. Correlation

4. Classification

5. Big Data

6. Clustering

7. Recommender Systems

8. Network Analysis

9. Time Series

10. Visualization

Index

Distributed graph computation with GraphX

GraphX (https://spark.apache.org/graphx/) is a distributed graph processing library that is designed to work with Spark. Like the MLlib library we used in the previous chapter, GraphX provides a set of abstractions that are built on top of Spark's RDDs. By representing the vertices and edges of a graph as RDDs, GraphX is able to process very large graphs in a scalable way.

We've seen in previous chapters how to process a large dataset using MapReduce and Hadoop. Hadoop is an example of a data-parallel system: the dataset is divided into groups that are processed in parallel. Spark is also a data-parallel system: RDDs are distributed across the cluster and processed in parallel.

Data-parallel systems are appropriate ways of scaling data processing when your data closely resembles a table. Graphs, which may have complex internal structure, are not most efficiently represented as tables. Although graphs can be represented as edge lists, as we've seen...

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Authors (1)

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.

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