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  • Book Overview & Buying Mastering Spark for Data Science
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Mastering Spark for Data Science

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
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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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Gauging oil prices


Now that we have a substantial amount of data in our data store (we can always add more data using the preceding Spark job) we will proceed to query that data, using the GeoMesa API, to get the rows ready for application to our learning algorithm. We could of course use raw GDELT files, but the following method is a useful tool to have available.

Using the GeoMesa query API

The GeoMesa query API enables us to query for results based upon spatio-temporal attributes, whilst also leveraging the parallelization of the data store, in this case Accumulo with its iterators. We can use the API to build SimpleFeatureCollections, which we can then parse to realize GeoMesa SimpleFeatures and ultimately the raw data that matches our query.

At this stage we should build code that is generic, such that we can change it easily should we decide later that we have not used enough data, or perhaps if we need to change the output fields. Initially, we will extract a few fields; SQLDATE, Actor1Name...

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Mastering Spark for Data Science
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