Fast Data Processing Systems with SMACK Stack

Key Features

• This highly practical guide shows you how to use the best of the big data technologies to solve your response-critical problems
• Learn the art of making cheap-yet-effective big data architecture without using complex Greek-letter architectures
• Use this easy-to-follow guide to build fast data processing systems for your organization

Book Description

SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing.

We'll start off with an introduction to SMACK and show you when to use it. First you'll get to grips with functional thinking and problem solving using Scala. Next you'll come to understand the Akka architecture. Then you'll get to know how to improve the data structure architecture and optimize resources using Apache Spark.

Moving forward, you'll learn how to perform linear scalability in databases with Apache Cassandra. You'll grasp the high throughput distributed messaging systems using Apache Kafka. We'll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspects of SMACK and you'll get the chance to practice these aspects of SMACK through a few study cases.

By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing.

You will start off with introduction to SMACK and when to use the same. In the later chapters you will be deep diving into the different aspects of SMACK. You will be starting with functional thinking and problem solving using Scala. You will understand Akka architecture. You will know how to improve the architecture and optimize resources using Apache Spark. You will learn how to make linear scalability in Databases with Apache Cassandra. You will understand the high throughput distributed messaging systems using Apache Kafka. You will learn how to build a cheap but effective cluster infrastructure with Apache Mesos. You will be able to practice these aspects of SMACk with few study cases.

By the end of the book you will be able to integrate all the components of the SMACK stack and use them together for highly effective and fast data processing.

What you will learn

• Build an affordable yet powerful cluster infrastructure
• Make queries, reports, and graphs based on your business' demands
• Manage and exploit unstructured and No-SQL data sources
• Use tools to monitor the performance of your architecture
• Integrate all the technology to decide which one is better than the other in replacing or reinforcing

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