CHAPTER 4User Experience
The Hadoop MapReduce program is designed to process a large amount of data at a low cost. Hadoop has been in use for almost 10 years, and it was initially used to focus on massive parallel processing. As covered in Chapter 3, however, it's tiresome to use the MapReduce program every time you need to process and analyze data. A lot of code, builds, and deploy processes are required to perform just a simple word count program, and developers get tired of frequently occurring repetitive tasks. And non-developers, such as data analysts or general users, who don't have a strong development background, struggle to use this method in their environment.
General users don't need to know every detail about MapReduce operating principles or shuffle phases, such as having to know the operational principles of Oracle, in order to extract the desired value from a database. The framework of Hadoop MapReduce makes developers concentrate on logic by dividing the work into fault-tolerance and node management. Hadoop-related projects, on the other hand, can provide an interface that you can use on data flows, regardless of any complex events.
This chapter focuses on how the Hadoop ecosystem works to improve user experiences. The Hadoop ecosystem has been developed continuously, so it benefits from cluster provisioning, ...
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