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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

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Part I: Introduction.- Chapter 1-Big Data, Big Problems.- Chapter 2-Big Data, Big Solutions.- Part II: Playing SMACK.- Chapter 3-The Language.- Chapter 4-The Engine.- Chapter 5-The Container.- Chapter 6-The View.- Chapter 7-The Storage.- Chapter 8-The Message Broker.- Part III: Improving SMACK.- Chapter 9-Enterprise Integration Patterns.- Chapter 10-Big Data Pipelines.- Chapter 11-Summary.
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.

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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).

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