Kick Start: Big Data is an E-Book about Big Data. A kick start is an ebook that readers can read within short amount and get started really fast without the need to invest days in reading a book. The target of Kick starts is to learn all the important things about a specific topic in a short and easy to read ebook. The first of this series is on Big Data. Readers will learn what Big Data is, what core technologies are involved and where you can go from there. Some technologies featured in this ebook are: Hadoop, NoSQL Databases, Data Storage techniques, Data analytic techniques and many more.

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Index:

Introduction to Big Data…………………………………………………………………. 7

  1. 1.1  Defining Big Data……………………………………………………………………. 7
  2. 1.2  Characteristics for Big Data……………………………………………………. 14

Challenges for Big Data ………………………………………………………………… 23

  1. 2.1  Storage Performance ……………………………………………………………. 23
  2. 2.2  Different Storage Systems …………………………………………………….. 25
  3. 2.3  Data partitioning and concurrency …………………………………………. 26
  4. 2.4  Moving Data for Analysis ………………………………………………………. 27

Creating Big Data Applications………………………………………………………. 29

3.1 Big Data Analysis iteration …………………………………………………….. 29

Big Data Management …………………………………………………………………. 32

4.1 Hardware Foundations …………………………………………………………. 32

  1. 4.1.1  Storage devices …………………………………………………………….. 32
  2. 4.1.2  Raid Systems ………………………………………………………………… 33
  3. 4.1.3  Requirements for private and public Cloud Solutions ………… 34

4.2 Data Storage and Software attributes …………………………………….. 39

  1. 4.2.1  Data Quality Attributes ………………………………………………….. 40
  2. 4.2.2  CAP Theorem ……………………………………………………………….. 42
  3. 4.2.3  Relational Database Management Systems ……………………… 45
  1. 4.2.4  NoSQL………………………………………………………………………….. 48
  2. 4.2.5  Hybrid RDBMS/NoSQL Systems ………………………………………. 52

Big Data Platforms ………………………………………………………………………. 55

5.1 Apache Hadoop……………………………………………………………………. 55

5.1.1 Hadoop Projects……………………………………………………………. 55

Big Data Analytics………………………………………………………………………… 58

  1. 6.1  Machine Learning…………………………………………………………………. 58
  2. 6.2  Data Mining…………………………………………………………………………. 58
  3. 6.3  Apache Mahout……………………………………………………………………. 60

Big Data Utilization………………………………………………………………………. 61

Appendix ……………………………………………………………………………………. 63

  1. 8.1  Table of Figures ……………………………………………………………………. 63
  2. 8.2  Table of Listings……………………………………………………………………. 64

References …………………………………………………………………………………. 65

 

Cover Image Copyright: Pete (https://www.flickr.com/photos/comedynose/) Cover Image Licensed under the Creative Commons License 2.0 (https://creativecommons.org/licenses/by/2.0/)

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