Complete Guide to Open Source Big Data Stack
☆☆☆☆☆
(0 reviews)
Lowest price (incl. delivery)
52,44 EUR
Typical price42,24 PLN
Lowest (90 days)37,44 PLN
Offers3
Last updated23시간 전
| 판매자 | Product price | Delivery | 합계 | 재고 여부 | Updated | |
|---|---|---|---|---|---|---|
| SP SpringerNatureLink Shop INT | 37,44 EUR | 15,00 EUR | 52,44 EUR | 구매 가능 | 1일 전 | View offer |
| SP SpringerNatureLink Shop INT | 49,99 USD | 19,00 USD | 68,99 USD | 구매 가능 | 23시간 전 | View offer |
| SP Springer Nature Author | 49,99 USD | free | 49,99 USD | 구매 가능 | 12시간 전 | View offer |
가격과 재고 여부는 변경될 수 있습니다. 마지막 업데이트: 08.08.2026 09:14.
0,0
☆☆☆☆☆
0 reviews
5★
0%
4★
0%
3★
0%
2★
0%
1★
0%
Product reviews
No reviews yet — be the first!
See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource management, processing, queuing, frameworks, data visualization, and more. What You’ll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for anyone interested in Hadoop or big data, and those experiencing problems with data size.