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GPUs, GPU Databases, and the future of BI
Would you like to be able to query massive multi-billion row datasets in seconds and interactively discover real-time, intelligent insights? Kinetica opens the way for BI tools such as Tableau to use GPU’s brute force compute and machine learning libraries such as TensorFlow, Caffe, and Torch along with Apache NiFi, Kafka, Spark and Spark Streaming, and Storm for fast OLAP, IoT, and streaming analytics.
Jonathan Greenberg — Senior Solutions Engineer
Jonathan Greenberg is a Senior Solutions Engineer at Kinetica for the NYC area. Jonathan came from ThoughtSpot, where he was the Technical Sales Engineer for New York Metro. Prior to ThoughtSpot, Jonathan worked as East Coast Head of Technology at Indix. Jonathan also spent five years with IBM and ten years with BMW of North America. Jonathan earned a BFA from Syracuse University, and lives in Woodcliff Lake, NJ with his wife Elicia, and 16 yr old daughter, Jade Anne. He enjoys training in Taekwondo with his daughter three times a week. He wants to make sure she gets her black belt before she starts dating.
Kinetica is a GPU-accelerated, in-memory analytics database that delivers truly real-time insights on large, complex and streaming data sets: 100x faster performance than traditional databases, in-database analytics via user-defined functions (UDFs) open the way for converged AI and BI workloads accelerated by GPUs. Kinetica also comes with native geospatial and visualization pipeline for interactive location-based analytics.
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