Hadoop Tutorial: Hadoop HBase
One of Apache Hadoop's chief advantages over the tradition of relational data models is that it shifts to accommodate information, rather than cramming ill-fitting data into a structured set of approaches. Harnessing big data presents several challenges for enterprising Hadoop adopters, not the least of which is effective storage and retrieval. At the same time, businesses don't want to waste resources figuring out how to best store their data without having assurances that the data will work for them. Concerns about usability have a significant effect on the progression of organizations toward data-driven insights, wrote Wired contributor Hannah Smalltree.
"To get value from big data, analysts need tools that can support rapid, ad-hoc analysis of large-scale datasets," she wrote.
What is Hadoop HBase?
Hadoop HBase is one of the central players in this scalable solution, designed expressly using an open-source framework to make sense of huge amounts of big data. As a NoSQL ('not only structured query language') database storage system, HBase offers a panoply of benefits for data warehousing, including real-time analysis of constantly growing data sets (think Twitter posts) and storage that is flexible and easy to access. It's no wonder that InformationWeek executive editor Doug Henschen called NoSQL databases the "practical workhorses of the big data revolution."
HBase is also a key player in the efficacy of Hadoop HDFS because of its fault-tolerant capacity – that is, if a system fails, HBase may slow down somewhat, but it won't stop completely. Much of the big data acquired and stored requires this sort of failsafe mechanism in place to avoid compromising large data sets, thereby making them less meaningful. Overall, HBase limits many of the factors that confound serious big data inquiries, like difficulty of user adoption and risk for data loss, making it an integral part of the Apache Hadoop ecosystem.