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  • Book Overview & Buying Frank Kane's Taming Big Data with Apache Spark and Python
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Frank Kane's Taming Big Data with Apache Spark and Python

Frank Kane's Taming Big Data with Apache Spark and Python

By : Frank Kane
3.8 (11)
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Frank Kane's Taming Big Data with Apache Spark and Python

Frank Kane's Taming Big Data with Apache Spark and Python

3.8 (11)
By: Frank Kane

Overview of this book

Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
Table of Contents (8 chapters)
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7
Where to Go From Here? – Learning More About Spark and Data Science

Improving the word-count script with regular expressions


The main problem with the initial results from our word-count script is that we didn't account for things such as punctuation and capitalization. There are fancy ways to deal with that problem in text processing, but we're going to use a simple way for now. We'll use something called regular expressions in Python. So let's look at how that works, then run it and see it in action.

Text normalization

In the previous section, we had a first crack at counting the number of times each word occurred in our book, but the results weren't that great. We had each individual word that had different capitalization or punctuation surrounding it being counted as a word of its own, and that's not what we want. We want each word to be counted only once, no matter how it's capitalized or what punctuation might surround it. We don't want duplicate words showing up in there. There are toolkits you can get for Python such as NLTK (Natural Language Toolkit...

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Frank Kane's Taming Big Data with Apache Spark and Python
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