Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka - Softcover

Estrada, Raul; Ruiz, Isaac

Softcover

ISBN 10: 1484221745 ISBN 13: 9781484221747

Publisher: Apress, 2016

Synopsis

Learn how to integrate full-stack open source big data architecture and to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer. 

Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses.

Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer:

The language: Scala

The engine: Spark (SQL, MLib, Streaming, GraphX)

The container: Mesos, Docker

The view: Akka

The storage: Cassandra

The message broker: Kafka

What You Will Learn:

• Make big data architecture without using complex Greek letter architectures
  
• Build a cheap but effective cluster infrastructure
  
• Make queries, reports, and graphs that business demands
  
• Manage and exploit unstructured and No-SQL data sources
  
• Use tools to monitor the performance of your architecture
  
• Integrate all technologies and decide which ones replace and which ones reinforce
  

Who This Book Is For:

Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer

About this title

About the Author

Raúl Estrada is the co-founder of Treu Technologies, an enterprise for Social Data Marketing and BigData research. He is an Enterprise Architect with more than 15 years of experience in cluster management and Enterprise Software. Prior to founding Treu Technologies, Estrada worked as an Enterprise Architect in Application Servers & evangelist for Oracle Inc. He loves functional languages like Elixir and Scala, and also has a Master of Computer Science degree.

Isaac Ruiz has been a Java programmer since 2001, and a consultant and architect since 2003. He has participated in projects of different areas and varied scopes (education, communications, retail, and others). Ruiz specializes in systems integration and has participated in projects mainly related to the financial sector. He is a supporter of free software. Ruiz likes to experiment with new technologies (frameworks, languages, methods).

From the Back Cover

Integrate full-stack open-source fast data pipeline architecture and choose the correct technology―Spark, Mesos, Akka, Cassandra, and Kafka (SMACK)―in every layer. Fast data is becoming a requirement for many enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases organizations need more than one paradigm to perform efficient analyses.

Big Data SMACK explains each technology and, more importantly, how to integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples. The book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by each technology. This book covers the five main concepts of data pipeline architecture and how to integrate, replace, and reinforce every layer:

• The engine: Apache Spark
• The container: Apache Mesos
The model: Akka<
• The storage: Apache Cassandra
• The broker: Apache Kafka

Edition details

• Publisher: Apress
• Publication date: 2016
• Language: English
• ISBN 10: 1484221745
• ISBN 13: 9781484221747
• Binding: Paperback
• Edition number: 1
• Number of pages: 289