Details zum E-Book

Fast Data Processing Systems with SMACK Stack. Click here to enter text

Fast Data Processing Systems with SMACK Stack. Click here to enter text

Raúl Estrada

E-book
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 developers have begun to use 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 aspect of SMACK using a few case studies.
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.
  • 1. Introducing SMACK
  • 2. Scala and Akka, the actor model
  • 3. Apache Spark, the engine recipes
  • 4. Apache Cassandra, the storage
  • 5. Apache Kafka, the message broker
  • 6. Apache Mesos, abstract Hardware
  • 7. Study case 1: SMACK and Bitcoin
  • 8. Study Case 2: SMACK and Twitter
  • 9. Study Case 3: SMACK and Geolocalization
  • Titel: Fast Data Processing Systems with SMACK Stack. Click here to enter text
  • Autor: Raúl Estrada
  • Originaler Titel: Fast Data Processing Systems with SMACK Stack. Click here to enter text.
  • ISBN: 9781786468062, 9781786468062
  • Veröffentlichungsdatum: 2016-12-22
  • Format: E-book
  • Artikelkennung: e_3aur
  • Verleger: Packt Publishing