Fast Data Processing Systems with SMACK Stack

Raul Estrada

• Editorial: Packt Publishing
• Año: 2016
• ISBN:
  9781786467201
• Edición: 1
• Categoría: Informatique
• Categoría: Modélisation et conception de données

Combine the incredible powers of Spark, Mesos, Akka, Cassandra, and Kafka to build data processing platforms that can take on even the hardest of your data troubles!

About This Book

• 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

Who This Book Is For

If you are a developer, data architect, or a data scientist looking for information on how to integrate the Big Data stack architecture and how to choose the correct technology in every layer, this book is what you are looking for.

What You Will Learn

• Design and implement a fast data Pipeline architecture
• Think and solve programming challenges in a functional way with Scala
• Learn to use Akka, the actors model implementation for the JVM
• Make on memory processing and data analysis with Spark to solve modern business demands
• Build a powerful and effective cluster infrastructure with Mesos and Docker
• Manage and consume unstructured and No-SQL data sources with Cassandra
• Consume and produce messages in a massive way with Kafka

In Detail

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.

Style and approach

With the help of various industry examples, you will learn about the full stack of big data architecture, taking the important aspects in every technology. You will learn how to integrate the technologies to build effective systems rather than getting incomplete information on single technologies. You will learn how various open source technologies can be used to build cheap and fast data processing systems with the help of various industry examples

Fast Data Processing Systems with SMACK Stack

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Fast Data Processing Systems with SMACK Stack

Copyright © 2016 Packt Publishing

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Published by Packt Publishing Ltd.

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Credits

Author Raúl Estrada	Copy Editor Safis Editing
Reviewers Anton Kirillov Sumit Pal	Project Coordinator Shweta H Birwatkar
Commissioning Editor Veena Pagare	Proofreader Safis Editing
Acquisition Editor Divya Poojari	Indexer Mariammal Chettiyar
Content Development Editor Amrita Noronha	Graphics Disha Haria
Technical Editor Sneha Hanchate	Production Coordinator Nilesh Mohite

About the Author

Raúl Estrada is a programmer since 1996 and Java Developer since 2001. He loves functional languages such as Scala, Elixir, Clojure, and Haskell. He also loves all the topics related to Computer Science. With more than 12 years of experience in High Availability and Enterprise Software, he has designed and implemented architectures since 2003.

His specialization is in systems integration and has participated in projects mainly related to the financial sector. He has been an enterprise architect for BEA Systems and Oracle Inc., but he also enjoys Mobile Programming and Game Development. He considers himself a programmer before an architect, engineer, or developer.

He is also a Crossfitter in San Francisco, Bay Area, now focused on Open Source projects related to Data Pipelining such as Apache Flink, Apache Kafka, and Apache Beam. Raul is a supporter of free software, and enjoys to experiment with new technologies, frameworks, languages, and methods.

I want to thank my family, especially my mom for her patience and dedication.

I would like to thank Master Gerardo Borbolla and his family for the support and feedback they provided on this book writing.

I want to say thanks to the acquisition editor, Divya Poojari, who believed in this project since the beginning.

I also thank my editors Deepti Thore and Amrita Noronha. Without their effort and patience, it would not have been possible to write this book.

And finally, I want to thank all the heroes who contribute (often anonymously and without profit) with the Open Source projects specifically: Spark, Mesos, Akka, Cassandra, and Kafka; an honorable mention for those who build the connectors of these technologies.

About the Reviewers

Anton Kirillov started his career as a Java developer in 2007, working on his PhD thesis in the Semantic Search domain at the same time. After finishing and defending his thesis, he switched to Scala ecosystem and distributed systems development. He worked for and consulted startups focused on Big Data analytics in various domains (real-time bidding, telecom, B2B advertising, and social networks) in which his main responsibilities were focused on designing data platform architectures and further performance and stability validation. Besides helping startups, he has worked in the bank industry building Hadoop/Spark data analytics solutions and in a mobile games company where he has designed and implemented several reporting systems and a backend for a massive parallel online game.

The main technologies that Anton has been using for the recent years include Scala, Hadoop, Spark, Mesos, Akka, Cassandra, and Kafka and there are a number of systems he’s built from scratch and successfully released using these technologies. Currently, Anton is working as a Staff Engineer in Ooyala Data Team with focus on fault-tolerant fast analytical solutions for the ad serving/reporting domain.

Sumit Pal has more than 24 years of experience in the Software Industry, spanning companies from startups to enterprises. He is a big data architect, visualization and data science consultant, and builds end-to-end data-driven analytic systems. Sumit has worked for Microsoft (SQLServer), Oracle (OLAP), and Verizon (Big Data Analytics). Currently, he works for multiple clients building their data architectures and big data solutions and works with Spark, Scala, Java, and Python. He has extensive experience in building scalable systems in middletier, datatier to visualization for analytics applications, using BigData and NoSQL DB. Sumit has expertise in DataBase Internals, Data Warehouses, Dimensional Modeling, As an Associate Director for Big Data at Verizon, Sumit, strategized, managed, architected and developed analytic platforms for machine learning applications. Sumit was the Chief Architect at ModelN/LeapfrogRX (2006-2013), where he architected the core Analytics Platform.

Sumit has recently authored a book with Apress - called - "SQL On Big Data - Technology, Architecture and Roadmap". Sumit regularly speaks on the above topic in Big Data Conferences across USA.

Sumit has hiked to Mt. Everest Base Camp at 18.2K feet in Oct, 2016. Sumit is also an avid Badminton pl...