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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Data modalities


From a modality perspective, all data can be grouped into three categories: structured, semi-structured, and unstructured. The modality is independent of the data source, organization, or storage technologies. In fact, different representations, organizations, and storage technologies perform well with, at the most, one modality. It is very difficult to efficiently support more than one modality.

  • Structured data is usually stored in databases, Oracle, HBase, Cassandra, and so on. Relational tables are the most commonly used organization and storage mechanism. Usually, structured data formats, data types, and sizes are fixed and well known.

  • Semi-structured data, as the name implies, has enough structure; however, there is also variability in its size, type, and format. The most common semi-structured formats are csv, json, and parquet.

  • Unstructured data, of course, is about 85 percent of the data we encounter. Images, audio files, and social media data all are unstructured. A...

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