Using In-memory Computing to Manage Big Data

In the telecommunications industry, for example, transactions are measured in the hundreds and thousands of transactions per second. Consider mobile prepaid databases that need to be checked every time a transaction is made. Does the subscriber belong to my network? Is the subscriber entitled to the service? Does the subscriber have enough air time? This easily translates to millions and billions of transactions on that particular database. Consider the emergence of real-time user profiling for contextual advertising. This requires a large amount of storage for easily retrievable transactional information used for profiling. As the industry grows larger, the volume of transactions also grows quickly. These use cases can also apply in the world of big data. This creates the need to build not just bigger and bigger but also faster and faster systems.

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