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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

MongoDB


MongoDB is a document-oriented database. It contains collections of documents. Each document is a JSON-like object:

{
    _id: ObjectId("558e846730044ede70743be9"),
    name: "Gandalf",
    age: 2000,
    pseudonyms: [ "Mithrandir", "Olorin", "Greyhame" ],
    possessions: [ 
        { name: "Glamdring", type: "sword" }, 
        { name: "Narya", type: "ring" }
    ]
}

Just as in JSON, a document is a set of key-value pairs, where the values can be strings, numbers, Booleans, dates, arrays, or subdocuments. Documents are grouped in collections, and collections are grouped in databases.

You might be thinking that this is not very different from SQL: a document is similar to a row and a collection corresponds to a table. There are two important differences:

  • The values in documents can be simple values, arrays, subdocuments, or arrays of subdocuments. This lets us encode one-to-many and many-to-many relationships in a single collection. For instance, consider the wizard collection. In SQL...

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