Big Data Importance

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Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data. Although big data promises better margin’s, more revenue and improvised operations it also brings new challenges to the It infrastructure which is “extreme data management” .At the same time these companies should also need to look at workload automation and make sure it is robust enough to make to handle the needs that the big data is associated to as well as the needs of the business intelligence it there to serve. File transfers requires to be scheduled for data to be moved to a central database or data warehouse. This by far involves an Extract, Transform and Load (ETL) workflow as data is usually gathered from different types of database. Once the data is brought to a central database, to determine patterns in the data queries are scheduled from a variety of users using various applications. The frequency of the queries varies from business to business – it can be continuous, once a day or hourly. And of course, as data gets added to the database and moved to new databases, there is the routine task of database management that needs to occur. Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar... ... middle of paper ... ...ns. Thus optimized systems to meet the challenges of extreme workloads are required by organizations because of all this. To offer packaged hardware and software solutions that are optimized for analytical processing has been the response given by the industries. One more important factor is to satisfy the business agility requirement which is an important factor in supporting analytical processing. In today’s business environment which is fast paced decisions required to be made faster and this is very important and plays a key role for the business success. For example in a fraud detection application the real time action is required. The ability to make decisions in real time to be more precise in few seconds or minutes rather than hours or days can be very important both financially as well as competitively although this is not true in case of all companies.

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