By now, you’ve likely seen the new images of Pluto, taken by the New Horizons spacecraft last week. Sending a man-made craft into the far reaches of our solar system would have seemed impossible a few years ago, but just last week, NASA reached Pluto to gather new insights and data. However, it didn’t happen without a mishap that could have derailed the full mission.

Just 10 days before New Horizons was scheduled to reach Pluto, mission control lost contact with its main computer systems. With communication remaining only with the spacecraft’s backup systems, NASA teams worked around the clock to get New Horizons back online and capable of taking the Pluto readings required for a successful mission. Three days later, the NASA team was able to get the main systems back online—only fourteen hours before they programmed the spacecraft for its encounter with Pluto.

So what happened? New Horizons’ main systems got overloaded: trying to compress newly gathered data while at the same time performing the other system functions it was programmed to do overworked the system. An overwhelmed computing architecture could have cost millions of dollars (or more) along with an invaluable loss of data and insight from a failed mission. The mission didn’t fail, however, and NASA was able to do something never before possible—reach Pluto to gather insight and data that up until recently wasn’t reasonably available.

The Pluto mission—and the ensuing mishap—is paralleled in today’s big data analytics systems. The computing power needed to process and analyze streaming and real-time information at the same time as batch data can often overwhelm today’s traditional x86 systems. This hampers organizations’ abilities to analyze and gain value from the data they have, and it can have a directly negative impact on the bottom line, corporate growth and innovation moving forward. And when you’re looking at industries like genomics and life sciences, this can mean a loss of life with delays in getting needed medicines to market or precision treatments available to those who need it.

While NASA had a team that could work around the clock to fix the computing issues, that isn’t feasible for a normal data analytics team. Additionally, when trying to do the impossible with your data—like reach Pluto to take new readings or bring a lifesaving drug to market—your data and analytics shouldn’t be hampered by a lack of computing power.

In the data analytics field, FPGA-accelerated systems, like the Ryft ONE, are helping to solve this compute problem by giving you breakthroughs—not bottlenecks. The Ryft ONE analyzes both batch and streaming data simultaneously and at super fast speeds, so when your company has its equivalent of the mission to Pluto, you aren’t stopped dead in your tracks by a computing system that can’t keep up with the task.

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