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.
Table of Contents
Chapter 1. Big Data, Big Problems
Chapter 2. Big Data, Big Solutions
Chapter 3. The Language: Scala
Chapter 4. The Model: Akka
Chapter 5. Storage. Apache Cassandra
Chapter 6. The View
Chapter 7. The Manager: Apache Mesos
Chapter 8. The Broker: Apache Kafka
Chapter 9. Fast Data Patterns
Chapter 10. Big Data Pipelines
Chapter 11. Glossary