How to tell if a GPU-oriented database is a good fit for your big data project
Image: iStock/stnazkul Hadoop is cool, and Spark is fast, but sometimes you need optimized hardware to handle increasingly bigger data workloads. That’s the premise behind Kinetica, an in-memory database that channels the power of massively distributed graphics processing units (GPUs) to promise 100-1,000x better real-time analytics performance. Such a promise is somewhat dizzying, given the bevy of big data analytics options available today. But it’s also a tad optimistic, given that GPUs are fantastic for workloads dependent on heavily parallelized matrix math, but not necessarily ideal for a wider range of big data applications. Not yet, anyway. The rise of GPUs in big data Kinetica (formerly GPUdb) has been around for several years, winning awards as it displaces Oracle and other industry heavyweights in significant deployments. First, there was the…
Link to Full Article: How to tell if a GPU-oriented database is a good fit for your big data project
How to tell if a GPU-oriented database is a good fit for your big data project
