At the core of Kinetica is a distributed, columnar, memory-first database designed for analytical (OLAP) workloads that runs on GPUs and CPUs. Kinetica combines threading and vectorized parallelism to achieve orders of magnitude performance gains versus threading alone. Data is stored across multiple nodes for parallel processing, with most frequently used data pre-loaded into RAM to eliminate IO bottlenecks.
Purpose-built to leverage extreme parallel computing power, such as GPUs and vectorized CPUs, Kinetica optimally routes query processing in each node across CPUs and GPUs for fastest results. Use industry standard SQL to query and analyze billions of rows of data in a matter of microseconds. Better compute efficiency is better resource efficiency, reducing your environmental impact.
Designed for enterprise scale, Kinetica can operate on your entire data corpus by intelligently managing data across GPU memory, system memory, SIMD, disk / SSD, HDFS, and cloud storage like S3 for optimal performance. Kinetica can also query and process data stored in data lakes, joining it with data managed by Kinetica in highly parallelized queries. Maintain your performance SLAs while slashing your data infrastructure spending.
Real-Time Streaming, Location, and Graph at Scale for Smart City Analysis
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