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You're reading from  Scala for Data Science

Product typeBook
Published inJan 2016
Reading LevelIntermediate
Publisher
ISBN-139781785281372
Edition1st Edition
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Author (1)
Pascal Bugnion
Pascal Bugnion
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Pascal Bugnion

Pascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University. Besides Scala, Pascal is a keen Python developer. He has contributed to NumPy, matplotlib and IPython. He also maintains scikit-monaco, an open source library for Monte Carlo integration. He currently lives in London, UK.
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Summary


By providing high-level concurrency abstractions, Scala makes writing parallel code intuitive and straightforward. Parallel collections and futures form an invaluable part of a data scientist's toolbox, allowing them to parallelize their code with minimal effort. However, while these high-level abstractions obviate the need to deal directly with threads, an understanding of the internals of Scala's concurrency model is necessary to avoid race conditions.

In the next chapter, we will put concurrency on hold and study how to interact with SQL databases. However, this is only temporary: futures will play an important role in many of the remaining chapters in this book.

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Scala for Data Science
Published in: Jan 2016Publisher: ISBN-13: 9781785281372

Author (1)

author image
Pascal Bugnion

Pascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University. Besides Scala, Pascal is a keen Python developer. He has contributed to NumPy, matplotlib and IPython. He also maintains scikit-monaco, an open source library for Monte Carlo integration. He currently lives in London, UK.
Read more about Pascal Bugnion