Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David George

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.

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Table of Contents (22 chapters)
Mastering Spark for Data Science
Mastering Spark for Data Science
Credits
Credits
Foreword
Foreword
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
The Big Data Science Ecosystem
The Big Data Science Ecosystem
Introducing the Big Data ecosystem
Overall architecture
Data technologies
Companion tools
Summary
2
Data Acquisition
Data Acquisition
Data pipelines
Content registry
Quality assurance
Summary
3
Input Formats and Schema
Input Formats and Schema
A structured life is a good life
GDELT dimensional modeling
Loading your data
Avro
Parquet
Summary
4
Exploratory Data Analysis
Exploratory Data Analysis
The problem, principles and planning
Preparation
Exploring GDELT
Summary
5
Spark for Geographic Analysis
Spark for Geographic Analysis
GDELT and oil
Formulating a plan of action
GeoMesa
Gauging oil prices
Summary
6
Scraping Link-Based External Data
Scraping Link-Based External Data
Building a web scale news scanner
Named entity recognition
GIS lookup
Names de-duplication
News index dashboard
Summary
7
Building Communities
Building Communities
Building a graph of persons
Using the Accumulo database
Community detection algorithm
GDELT dataset
Summary
8
Building a Recommendation System
Building a Recommendation System
Different approaches
Uninformed data
Building a song analyzer
Building a recommender
Summary
9
News Dictionary and Real-Time Tagging System
News Dictionary and Real-Time Tagging System
The mechanical Turk
Designing a Spark Streaming application
Consuming data streams
Processing Twitter data
Fetching HTML content
Using Elasticsearch as a caching layer
Classifying data
Our Twitter mechanical Turk
Summary
10
Story De-duplication and Mutation
Story De-duplication and Mutation
Detecting near duplicates
Building stories
Story mutation
Summary
11
Anomaly Detection on Sentiment Analysis
Anomaly Detection on Sentiment Analysis
Following the US elections on Twitter
Analysing sentiment
Using Timely as a time series database
Twitter and the Godwin point
A Small Step into sarcasm detection
Summary
12
TrendCalculus
TrendCalculus
Studying trends
The TrendCalculus algorithm
Practical applications
Summary
13
Secure Data
Secure Data
Data security
Authentication and authorization
Access
Encryption
Data disposal
Kerberos authentication
Security ecosystem
Your Secure Responsibility
Summary
14
Scalable Algorithms
Scalable Algorithms
General principles
Spark architecture
Challenges
Plotting your course
Design patterns and techniques
Summary

Spark architecture

Apache Spark is designed to simplify the laborious, and sometimes error prone task of highly-parallelized, distributed computing. To understand how it does this, let's explore its history and identify what Spark brings to the table.

History of Spark

Apache Spark implements a type of data parallelism that seeks to improve upon the MapReduce paradigm popularized by Apache Hadoop. It extended MapReduce in four key areas:

  • Improved programming model: Spark provides a higher level of abstraction through its APIs than Hadoop; creating a programming model that significantly reduces the amount of code that must be written. By introducing a fluent, side-effect-free, function-oriented API, Spark makes it possible to reason about an analytic in terms of its transformations and actions, rather than just sequences of mappers and reducers. This makes it easier to understand and debug.

  • Introduces workflow: Rather than chaining jobs together (by persisting results to disk and using a third...

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