Whenever you believe that you are finding it tough to ask
questions of your data, you should immediately consider the idea of leveraging
SQL-MapReduce. Just to let you know, you won’t only be remedying the problem of
not being able to ask questions but may also succeed in ensuring that the
results are comparatively fast. Of course, when both of your problems are
solved you will no longer be scared of service level agreements because the
latter will now be met without fail. Meanwhile, since are yet to experiment
with SQL-MapReduce, you may not know that for what type of requirements you
should actually contemplate this whole thing. Given below are some examples,
for your reference:
- Fraud detection: Frauds can make you lose more money than actually make out of your business. Therefore, they have to be detected under all circumstances, or else you are at the risk of suffering huge losses. Unfortunately, all this while, you’d have probably struggled to detect cases of fraud because your queries may not have come in handy. However, when you firmly believe that detecting frauds is one of the many SQL-MapReduce applications, you’d be able to catch more cases of fraud than ever before. Furthermore, there is an added advantage and it lies in the fact that your fraud analytics cycle time will get reduced by a considerable proportion.
- Graph analysis: While trying to serve your customers in the best possible way you’d have to take help of several analytical techniques and procedures, with graph analysis being one of them. Interestingly, graph analysis is one of the perfect and most viable examples of SQL-MapReduce applications because you get to achieve the desired results with ease. For instance, when you are already in the process of leveraging social media to understand the customer behavior, SQL-MapReduce functions will ensure that you actually get to have a deeper understanding of the connection between your customers.
- Search behavior: It is undeniable that no matter what type of business you are running, you’d have at least some customers who are habitual of conducting an online search. Imagine, if you get to understand the search patterns or the ones followed by the customers after they are conducting the search, you’d have some new information with you.
Nevertheless, while experimenting with SQL-MapReduce, you
may come across cases where you might require exploiting the benefits of both column
and row stores. In such cases, depending on a hybrid data store is probably the
most sensible thing to do.
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