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Big Data SMACK A Guide to Apache Spark (sách keo gáy, bìa mềm)
Categories:Computers
Year:2016
Edition:1
Language:english
Pages:277
This book is about how to integrate full-stack open source big data
architecture and how 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 datasets 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. The 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’ll learn
How to make big data architecture without using complex Greek letter architectures.
How to build a cheap but effective cluster infrastructure.
How to make queries, reports, and graphs that business demands.
How to manage and exploit unstructured and No-SQL data sources.
How use tools to monitor the performance of your architecture.
How to integrate all technologies and decide which replace and which reinforce.
Who This Book Is For
This book is for developers, data architects, and data scientists
looking for how to integrate the most successful big data open stack
architecture and how to choose the correct technology in every layer.
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