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Scala for Data Science, by Pascal Bugnion
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Leverage the power of Scala with different tools to build scalable, robust data science applications
About This Book- A complete guide for scalable data science solutions, from data ingestion to data visualization
- Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations
- Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided
If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions.
What You Will Learn- Transform and filter tabular data to extract features for machine learning
- Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines
- Read, transform, and write data to both SQL and NoSQL databases in a functional manner
- Write robust routines to query web APIs
- Read data from web APIs such as the GitHub or Twitter API
- Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements
- Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations
- Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive
Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines.
This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala.
Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala's emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks.
This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions.
Style and approachA tutorial with complete examples, this book will give you the tools to start building useful data engineering and data science solutions straightaway
- Sales Rank: #866758 in Books
- Published on: 2016-01-28
- Released on: 2016-01-28
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .94" w x 7.50" l, 1.57 pounds
- Binding: Paperback
- 416 pages
About the Author
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.
Most helpful customer reviews
4 of 4 people found the following review helpful.
A practical approach to taming data science with Scala's functional paradigm
By adnan baloch
One of the hottest jobs these days is that of the data scientist. It makes sense given the explosion of data generated by the online activities of millions of internet users and collected by online businesses and social media websites. As the author of this book explains, data scientists need to be conversant in three areas at once: programming, statistics/numerical algorithms and the ability to ask the right questions that will help in making decisions crucial to expanding a business and keeping it competitive. This book deals with the first of these essential skills: programming. Scala is a functional programming language with powerful parallel computing capabilities. The functional part of the language ensures that code written in Scala is terse and avoids common bugs that are the major source of headaches in traditional languages like Python or Java. The one place where Scala lags is in the availability of mature libraries. Still, the author discusses several good Scala libraries that make the Scala programmer's job easy so she can focus on the actual data science. Breeze and Breeze-viz are put to use in manipulating arrays of data and plotting simple graphs respectively. Parallel collections are explained intuitively so that anyone without any experience of parallel computation will find it useful. Futures make it possible to add further concurrency to Scala based projects by freeing the main thread from blocking events like waiting to receive data from a web page.
Databases form the core of data storage in any data focused programming solution. The author shows how to write a functional wrapper for JDBC and also discusses a popular functional wrapper called Slick so the readers will be equipped to handle both scenarios depending on their needs. Gathering data from the web can hardly work without an understanding of interfacing with APIs. The author takes a very practical approach in exploring this crucial aspect by querying the Github API and storing the data in MongoDB. Furthermore, readers get to see how to create their own simple web API. Sooner or later, data scientists have to turn to distributed computing for the horsepower needed to complete their complex calculations. Actor based concurrency using Akka fills this gap and the author gives it an excellent treatment in a dedicated chapter. Machine learning is discussed using MLlib but a good conceptual understanding of ML is needed for this chapter. The uninitiated are forewarned: don't expect the author to teach machine learning in a single chapter. For me, the most exciting two chapters are the ones that use the Play framework with D3.js to build a single page app. This represents true empowerment because it enables budding data scientists to share their fruits of labor with the entire web community in a visually captivating way. In short, data scientists wondering about Scala's effectiveness as a great tool for data science need only skim through this book. They won't be disappointed.
3 of 3 people found the following review helpful.
Scala for Data Science was a fairly good introduction for me to applied Scala applications and interoperability
By DWR
Scala for Data Science was a fairly good introduction for me to applied Scala applications and interoperability. Working through a few examples in this book proved to be my first foray into using Scala. In my opinion, the book seemed a bit light on techniques for statistical learning, but was rich in tools showing how to Scala with JSON, APIs, SQL, MongoDB, and Spark.
2 of 2 people found the following review helpful.
Great book!
By Timothy J. Whittaker
I spent a lot of time looking for a book like this. The other reviewer is correct, there is very little on actual statistical learning in this text, but this is not the author's aim. To me, this is more about awareness of some great Scala (and Java) libraries (with application) that any data scientist should find useful. The definition of data science taken by this book is probably the broadest I have seen - there is something worthwhile in every single chapter of this book.
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