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Python Data Science Essentials

You're reading from   Python Data Science Essentials A practitioner's guide covering essential data science principles, tools, and techniques

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789537864
Length 472 pages
Edition 3rd Edition
Languages
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
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Toc

Table of Contents (11) Chapters Close

Preface 1. First Steps FREE CHAPTER 2. Data Munging 3. The Data Pipeline 4. Machine Learning 5. Visualization, Insights, and Results 6. Social Network Analysis 7. Deep Learning Beyond the Basics 8. Spark for Big Data 9. Strengthen Your Python Foundations 10. Other Books You May Enjoy

From a standalone machine to a bunch of nodes

Handling big data is not just a matter of size; it's actually a multifaceted phenomenon. In fact, according to the 3V model (volume, velocity and variety), systems operating on big data can be classified using three (orthogonal) criteria:

  • The first criterion to consider is the velocity that the system achieves to process the data. Although a few years ago, speed was used to indicate how quickly a system was able to process a batch, nowadays, velocity indicates whether a system can provide real-time outputs on streaming data.
  • The second criterion is volume; that is, how much information is available to be processed. It can be expressed in the number of rows or features, or just a bare count of the bytes. In streaming data, the volume indicates the throughput of data arriving in the system.
  • The last criterion is variety; that is...
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