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Scalable Big Data Architecture
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Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution.
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Azarmi Scalable Big Data Architecture
Scalable Big Data
Architecture
A practitioner’s guide to choosing relevant
big data architecture
—
Bahaaldine Azarmi
Scalable Big Data Architecture
BOOKS FOR PROFESSIONALS BY PROFESSIONALS® THE EXPERT’S VOICE® IN BIG DATA
This book highlights the diff erent types of data architecture and illustrates the many possibilities
hidden behind the term “Big Data”, from the usage of NoSQL databases to the deployment of
stream analytics architecture, machine learning, and governance.
Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage
complex distributed applications, which involve web applications, RESTful API, and high
throughput of large amount of data stored in highly scalable NoSQL data stores such as
Couchbase and Elasticsearch. This book demonstrates how data processing can be done at
scale from the usage of NoSQL datastores to the combination of big data distribution.
When the data processing is too complex and involves diff erent processing topology like long
running jobs, stream processing, multiple data sources correlation, and machine learning,
it’s o en necessary to delegate the load to Hadoop or Spark and use the NoSQL to serve
processed data in real time.
This book shows you how to choose a relevant combination of big data technologies available
within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns,
log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use
diff erent open source projects such as Logstash, Spark, Ka a, and so on.
Traditional data infrastructures are built for digesting and rendering data synthesis and
analytics from a large amount of data. This book helps you to understand why you should
consider using machine learning algorithms early on in the project, before being overwhelmed
by constraints imposed by dealing with the high throughput of big data.
Scalable Big Data Architecture is for developers, data architects, and data scientists looking for
a better understanding of how to choose the most relevant pattern for a big data project and
which tools to integrate into that pattern.
US $39.99
Shelve in:
Databases/Data Warehousing
User level:
Beginning–Advanced
SOURCE CODE ONLINE
9781484 213278
53999
ISBN 978-1-4842-1327-8
www.it-ebooks.info
Scalable Big Data Architecture
Copyright © 2016 by Bahaaldine Azarmi
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now
known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with
reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed
on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or
parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its
current version, and permission for use must always be obtained from Springer. Permissions for use may be
obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under
the respective Copyright Law.
ISBN-13 (pbk): 978-1-4842-1327-8
ISBN-13 (electronic): 978-1-4842-1326-1
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with
every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an
editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
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proprietary rights.
While the advice and information in this book are believed to be true and accurate at the date of publication,
neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or
omissions that may be made. The publisher makes no warranty, express or implied, with respect to the
material contained herein.
Managing Director: Welmoed Spahr
Lead Editor: Celestin Suresh John
Development Editor: Douglas Pundick
Technical Reviewers: Sundar Rajan Raman and Manoj Patil
Editorial Board: Steve Anglin, Pramila Balen, Louise Corrigan, Jim DeWolf, Jonathan Gennick,
Robert Hutchinson, Celestin Suresh John, Michelle Lowman, James Markham, Susan McDermott,
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v
Contents at a Glance
About the Author ����������������������������������������������������������������������������������������������������� xi
About the Technical Reviewers ����������������������������������������������������������������������������� xiii
■ Chapter 1: The Big (Data) Problem ������������������������������������������������������������������������ 1
■Chapter 2: Early Big Data with NoSQL ����������������������������������������������������������������� 17
■Chapter 3: Defining the Processing Topology ������������������������������������������������������ 41
■Chapter 4: Streaming Data ���������������������������������������������������������������������������������� 57
■Chapter 5: Querying and Analyzing Patterns ������������������������������������������������������� 81
■Chapter 6: Learning From Your Data? ��������������������������������������������������������������� 105
■Chapter 7: Governance Considerations ������������������������������������������������������������� 123
Index ��������������������������������������������������������������������������������������������������������������������� 139
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