Frequently asked questions about Column-oriented storage techniques
Lately, it has been observed that businesses thriving on MapReduce and similar platforms have begun to use column-oriented storage techniques. Perhaps they have realized that these techniques can result in a comparatively efficient I/O, and bring along a couple of benefits. To begin with, these are known to make the computation of an aggregate a lot easier, and are often used when a small subset of data is involved. These have also been found useful in case of business analytics and other applications of this kind.
Despite the aforementioned benefits, the popularity of these techniques is somewhat stagnant; in other words, these are yet to cause a stir. Nevertheless, all is not lost; the situation can certainly improve if businesses are familiarized with the nuances of these techniques. Perhaps the answers to the following questions would provide a deeper insight:
What is the need for column-oriented storage techniques?
To begin with, these techniques are required for the preparation of canned business intelligence reports in an easy manner; needless to say, these reports are indispensable for any sort of analysis. Furthermore, these can play a major role in obtaining an efficient I/O, especially if quite a less amount of attributes are involved.
What are the benefits of using these techniques?
First things first; these are quite efficient, especially when each and every row is provided with new values for a column. Nevertheless, there are plenty of benefits; some of the noticeable ones are as follows:
- These can be easily used for simple as well as complex computations; in fact, total sales and other important factors can be analyzed with utter ease using these techniques.
- These form the basis of many important activities including reporting, data mining, and fraud detection, and make it easy for a business to streamline its data.
- Lately, these have also been found beneficial for series analysis as well as many other analytical tasks.
What role do these play in business analytics(BA)?
It is a known fact that BA is very much dependent on the accumulation of relevant Big data analytics, and involves the development of new insights on the basis of this data. Furthermore, it calls for an in-depth analysis of data and other statistical information; therefore, it is important that the data has been stored properly in the first place. These techniques can verily help a business in streamlining the storage process, and accessing the data, as and when required.
There are times when an organization has no option but to resort to deeper analytics. In such a scenario, the columnar approach might not come across as the most feasible solution. In fact, most of the companies prefer to go the conventional way, and use rows under such circumstances.
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