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Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
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Matthew Hallett
Antoine Amend Antoine Amend
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Antoine Amend
Andrew Morgan Andrew Morgan
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Andrew Morgan
Albert Bifet Albert Bifet
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Albert Bifet
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Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

Using Timely as a time series database

Now that we are able to transform raw information into a clean series of Twitter sentiment with parameters such as hashtags, emojis, or US states, such a time series should be stored reliably and made available for fast query lookups.

In the Hadoop ecosystem, OpenTSDB (http://opentsdb.net/) is the default database for storing millions of chronological data points. However, instead of using the obvious candidate, we will introduce one you may not have come across before, called Timely (https://nationalsecurityagency.github.io/timely/). Timely is a recently open sourced project started by the National Security Agency (NSA), as a clone of OpenTSDB, which uses Accumulo instead of HBase for its underlying storage. As you may recall, Accumulo supports cell-level security, and we will see this later on.

Storing data

Each record is composed of a metric name (for example, hashtag), timestamp, metric value (for example, sentiment), an associated set of tags (for...

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