Big Data SMACK, 1st ed.
A Guide to Apache Spark, Mesos, Akka, Cassandra, and Kafka

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Language: English

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264 p. · 17.8x25.4 cm · Paperback

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

    Part 1. Introduction

    Chapter 1. Big Data, Big Problems

    Chapter 2. Big Data, Big Solutions

    Part 2. Playing SMACK

    Chapter 3. The Language: Scala

    Chapter 4. The Model: Akka

    Chapter 5. Storage. Apache Cassandra

    Chapter 6. The View

    Chapter 7. The Manager: Apache Mesos

    Chapter 8. The Broker: Apache Kafka

    Part 3. Improving SMACK

    Chapter 9. Fast Data Patterns

    Chapter 10. Big Data Pipelines

    Chapter 11. Glossary.

    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).

    The first book presenting the SMACK stack

    A practical guide teaching how to incorporate big data

    Covers the full stack of big data architecture, discussing the practical benefits of each technology