Complete Guide to Open Source Big Data Stack-Apress(2018).pdf下载

I have developed this book to investigate Mesos-based cluster development and integration. I found that data center operating system (DCOS; and it’s command-line interface [CLI]) was a natural progression from basic Mesos; so you will find that the later chapters of this book concentrate on that. Within the limits of the funding and time available to me, I have investigated each element of a Mesos-based big data stack, starting with a local cloud on Apache CloudStack followed by Apache *lyn for release management. Chapters are topic specific covering Mesos-based resource management, storage, processing, and queueing. I examine application frameworks like Akka and Netty; and finally, I cover visualisation. As with previous book projects, I have taken an integration-based approach, investigating how to make systems work together. I found that it was quite a challenge to create a viable and reliable DCOS-based cluster, but the result was worth the effort. DCOS provides a functionally rich and robust system once the learning curve is mastered. This book is aimed at anyone who is interested in big data stacks based on Apache Mesos and Spark. It would be useful to have some basic knowledge of Centos Linux and Scala. But don’t be deterred if you don’t; I believe that if you are interested in these topics and willing to learn, you will succeed. Most chapters contain examples that you can follow to gain a better understanding. I would advise completing the practical examples yourself to increase confidence. This book covers each topic to the extent that time and resources have allowed. Having completed the book, I am aware that there are many other topics that I would have liked to have examined such as DCOS framework development, Mesos framework intercommunication, and *lyn releases to DCOS. I hope that I will be able to address these topics at some point in the future. In the first chapter, I will provide a fuller introduction to the book architecture and chapter contents. I will describe the big data stack structure as well as extended topics such as scaling and “cloud or cluster.”