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Everyone has heard the old adage that time is money. In today’s society, business moves at the speed of making a phone call, looking something up online via your cell phone, or posting a tweet. So, when time is money (and can be a lot of money), why are businesses okay with waiting weeks or even months to get valuable information from their data?

The answer is they likely aren’t okay with it, but they seem resigned to being stuck with the status quo. The current data pipeline is reliant on legacy x86 architectures, which are littered with bottlenecks — ranging from processing power limitations, transforming and loading data, expensive data indexing operations, relatively slow networking speeds, and complex software stacks. What should be a simple and quick action has turned into one bogged down by lengthy and complex processes. Solutions such as Hadoop and Spark have been created (and revised over and over again) to try and solve the problem; however, to get the performance required, organizations have to scale out to massive clusters, which leads to a new set of bottlenecks. The already-slow status quo is made even slower for businesses that have more than just a few megabytes of data to store and analyze.

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