English | 2017 | ISBN: 978-1785882142 | 560 Pages | EPUB, AZW, PDF (conv) | 26 MB
Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products
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.
What You Will Learn
- Learn the design patterns that integrate Spark into industrialized data science pipelines
- See how commercial data scientists design scalable code and reusable code for data science services
- Explore cutting edge data science methods so that you can study trends and causality
- Discover advanced programming techniques using RDD and the DataFrame and Dataset APIs
- Find out how Spark can be used as a universal ingestion engine tool and as a web scraper
- Practice the implementation of advanced topics in graph processing, such as community detection and contact chaining
- Get to know the best practices when performing Extended Exploratory Data Analysis, commonly used in commercial data science teams
- Study advanced Spark concepts, solution design patterns, and integration architectures
- Demonstrate powerful data science pipelines