While a hierarchical data warehouse stores data in files of folders, a typical Hadoop based system relies on a flat architecture to store your data. Without proper data governance or a clear understanding of what your data is all about, there is an undeniable chance of turning data lakes into swamps, where an interesting dataset such as GDELT would be nothing more than a folder containing a vast amount of unstructured text files. For that reason, data classification is probably one of the most widely used machine learning techniques in large scale organizations as it allows users to properly categorize and label their data, publish these categories as part of their metadata solutions, and therefore access specific information in the most efficient way. Without a proper tagging mechanism executed upfront, ideally at ingest, finding all news articles about a specific topic would require parsing the entire dataset looking for specific...
Mastering Spark for Data Science
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Mastering Spark for Data Science
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Overview of this book
Data science seeks to transform the world using data, and this is typically achieved
through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.
This book deep dives into using Spark to deliver production-grade data science
solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.
You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
The Big Data Science Ecosystem
Data Acquisition
Input Formats and Schema
Exploratory Data Analysis
Spark for Geographic Analysis
Scraping Link-Based External Data
Building Communities
Building a Recommendation System
News Dictionary and Real-Time Tagging System
Story De-duplication and Mutation
Anomaly Detection on Sentiment Analysis
TrendCalculus
Secure Data
Customer Reviews