Big Data SMACK: A Guide to Apache Spark, Mesos, Akka, Cassandra and Kafka
Год издания: 2016
Автор: Raul Estrada, Isaac Ruiz
Издательство: Apress Media
ISBN: 9781484221747
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 264
Исходники:
GitHub
Описание:
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.
