The Elephant in the room is real. Normally, this would be a bad thing as the idiom implies a large object that cannot be ignored but is being avoided. Here, I refer to corporate structured data that is the fuel of its main activities, and whose problems with accuracy and trustworthiness are past the stage of being alleged. This includes financial, customer, company, inventory, medical, risk, supply chain, and other primary data used for decision-making, applications, reports, and Business Intelligence. This is Small Data relative to the much-ballyhooed Big Data of the Terabyte range.
Yet, Small Data feeds mission critical activities whereas Big Data is most often targeting value-added functions. I say activities instead of applications because the business, including Government, is mostly concerned with the human use of data in concert with automated computer applications to provide services to other people, design and build new things, and manage the business itself for efficiency, effectiveness, security, and finances. Small Data does not meet the entry requirements of Big Data: it is often tens to hundreds of Gigabytes at its largest; it has been well dissected and managed assets of individual data elements and does not require unstructured images or documents; has a large group of people involved in its lifecycle; and is commonly subject to laws, regulations, and policies.
Small Data has fed 30 years of Information Technology market growth for established companies like IBM, Oracle, Informatica, and Teradata. The market continued to grow to support the expanding use and importance of the data to daily management activities and new automation and analytic applications. However, this was typically done in a minimalist manner with coordination, correlation, documentation, and cohesive management left for a future time when there would be ample resources of people, time, money, and skills to practice full-blown engineering as done in space travel. Most of us are still waiting for the future to arrive.
Enter Hadoop. Hadoop originated for truly big data with its new requirements for very large storage and processing, and the desire to handle this without the very high cost in people, hardware, and software that was the status quo in early 2000’s. Hadoop is a superb computer engineering feat that has significantly pushed the price point down and the accessibility to distributed computing up. It arose from a particular use case, search engine content digestion and querying, and was inevitably tuned for this type of use. This was Hadoop 1 with the intuitively titled MapReduce engine and Hadoop Distributed File System (HDFS).
As with all powerful new technology, people started getting creative and wanted to apply it to other uses. Also, in the United States, we still value innovation for profit. Yes, we do. Deal with it.
So, looking for sources of profit, the growing ecosystem locked on to corporate data processing. Enter the need for more general purpose application processing, more flexible data storage, and clearer less computer science oriented user interfaces. Welcome Hadoop 2. This now general purpose distributed computing environment is filled with smartly designed and built components that amazingly work well together. But, gone are the intuitive names with components like Oozie, Flume, Zookeeper, Hive, Thrift, Sqoop, and Mahout. One obvious and excellent analog of the core technology to a long-standing business field is processing vast amounts of data as a modernized form of business analytics for marketing and customer targeting.
Nowadays Hadoop is very important for any business may be it’s a small business or big business.The industry needs Hadoop guys, people with knowledge of Hadoop have huge demand so what are you waiting for just learn it and earn it. Tek classes providing the best Hadoop online and classroom training in the industry with real time experts and case studies. For more details go through this link. BIG DATA HADOOP TRAINING