Batch processing has traditionally been seen as the most efficient way to process large volumes of data and is often used in instances when programs need to group transactions during a period of time. Batch processing inputs the transactions as a data set, simultaneously processing that data, and then produces an output set of data. The challenge with batch processing is that when applied to the same data sets, it inherently leads to time delays and latency. For these situations, real-time processing is a better bet.
Real-time processing enables a continual stream of data input and analysis to ensure data are up-to-date. For data-driven decisions that demand up-to-the-minute accuracy, real-time processing is the way to go. This can be applicable across industries, from online retailers trying to provide the most relevant advertisements to customers during a sale, to relief agencies that need the most up-to-date data to provide effective and urgent services.
Database technologies are rapidly evolving to a degree where it can be difficult to keep up with the newest solutions and buzzwords, let alone distinguish one from another. Looking for the right solution can be especially challenging when IT vernacular is constantly putting terms at odds. There are countless “this-or-that” conversations in database technology. In this slideshow, Kurt Dobbins, CEO of Deep Information Sciences, takes a look at a few of the most common faceoffs.