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
