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Mastering Spark for Data Science

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
4 (2)
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Mastering Spark for Data Science

Mastering Spark for Data Science

4 (2)
By: Bifet, Morgan, Amend, Hallett, 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.
Table of Contents (15 chapters)
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Challenges

Now that we have gained an understanding of the Spark architecture, let's prepare for writing scalable analytics by introducing some of the challenges, or gotchas that you might face if you're not careful. Without knowledge of these up-front, you could lose time trying to figure them out on your own!

Algorithmic complexity

As well as the obvious effect of the size of your data, the performance of an analytic is highly dependent on the nature of the problem you're trying to solve. Even some seemingly simple problems, such as a depth first search of a graph, do not have well-defined algorithms that perform efficiently in distributed environments. This being the case, great care should be taken when designing analytics to ensure that they exploit patterns of processing that are readily parallelized. Taking the time to understand the nature of your problem in terms of complexity before you start, can pay off in the long term. In the next section, we'll show you...

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