By Pascal Bugnion
ISBN-10: 1785281372
ISBN-13: 9781785281372
Leverage the facility of Scala with various instruments to construct scalable, powerful info technology applications
About This Book
- A whole consultant for scalable facts technological know-how strategies, from facts ingestion to information visualization
- Deploy horizontally scalable info processing pipelines and make the most of net frameworks to construct attractive visualizations
- Build sensible, type-safe workouts to have interaction with relational and NoSQL databases with assistance from tutorials and examples provided
Who This ebook Is For
If you're a Scala developer or info scientist, or in an effort to input the sector of information technology, then this ebook provides you with all of the instruments you must enforce facts technological know-how solutions.
What you'll Learn
- Transform and clear out tabular facts to extract gains for computer learning
- Implement your personal algorithms or reap the benefits of MLLib's wide suite of types to construct allotted computer studying pipelines
- Read, rework, and write info to either SQL and NoSQL databases in a useful manner
- Write powerful workouts to question internet APIs
- Read facts from net APIs resembling the GitHub or Twitter API
- Use Scala to have interaction with MongoDB, which bargains excessive functionality and is helping to shop huge info units with doubtful question requirements
- Create Scala internet purposes that couple with JavaScript libraries corresponding to D3 to create compelling interactive visualizations
- Deploy scalable parallel functions utilizing Apache Spark, loading facts from HDFS or Hive
In Detail
Scala is a multi-paradigm programming language (it helps either object-oriented and practical programming) and scripting language used to construct functions for the JVM. Languages similar to R, Python, Java, etc are as a rule used for facts technology. it truly is quite sturdy at reading huge units of information with none major impression on functionality and hence Scala is being followed by means of many builders and knowledge scientists. facts scientists should be acutely aware that development purposes which are actually scalable is difficult. Scala, with its robust sensible libraries for interacting with databases and development scalable frameworks provide you with the instruments to build powerful information pipelines.
This booklet will introduce you to the libraries for consuming, storing, manipulating, processing, and visualizing facts in Scala.
Packed with real-world examples and fascinating information units, this publication will train you to ingest facts from flat documents and internet APIs and shop it in a SQL or NoSQL database. it's going to help you layout scalable architectures to procedure and modelling your info, ranging from easy concurrency constructs comparable to parallel collections and futures, via to actor platforms and Apache Spark. in addition to Scala's emphasis on useful constructions and immutability, you'll how one can use the fitting parallel build for the task handy, minimizing improvement time with no compromising scalability. eventually, you are going to how you can construct attractive interactive visualizations utilizing net frameworks.
This booklet supplies tutorials on essentially the most universal Scala libraries for info technological know-how, permitting you to quick wake up to hurry with construction information technology and knowledge engineering solutions.
Style and approach
A educational with whole examples, this booklet provides you with the instruments to begin construction important facts engineering and knowledge technology strategies straightaway
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Additional resources for Scala for Data Science
Example text
The basic linear algebra operations underlying Breeze rely on the netlib-java library, which can use system-optimized BLAS and LAPACK libraries, if present. Thus, linear algebra operations in Breeze are often extremely fast. Breeze is still undergoing rapid development and can, therefore, be somewhat unstable. Vectors Breeze makes manipulating one- and two-dimensional data structures easy. 0) We have just defined a three-element vector, v. Vectors are just one-dimensional arrays of data exposing methods tailored to numerical uses.
If you are writing a throwaway script or exploring data, you might be better served with Python. If you are writing something that will need to be reused and requires a certain level of provable correctness, you will find Scala extremely powerful. Summary Now that the obligatory introduction is over, it is time to write some Scala code. In the next chapter, you will learn about leveraging Breeze for numerical computations with Scala. For our first foray into data science, we will use logistic regression to predict the gender of a person given their height and weight.
We will calculate the probability of getting at least 60 heads out of 100 coin tosses. We can estimate this using Monte Carlo: we simulate 100 coin tosses by drawing 100 random Boolean values and check whether the number of true values is at least 60. We repeat this until results have converged to the required accuracy, or we get bored of waiting. nextBoolean() // count the number of heads } trial: Int [ 10 ] Chapter 1 The trial function runs a single set of 100 throws, returning the number of heads: scala> trial Int = 51 To get our answer, we just need to repeat trial as many times as we can and aggregate the results.
Scala for Data Science by Pascal Bugnion
by Jason
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