combines a distributed, in-memory, GPU-accelerated database with streaming analytics, location intelligence, and machine learning. The database — vectorized, columnar, memory-first, and designed for analytical (OLAP) workloads — automatically distributes any workload across CPUs and GPUs. It uses SQL-92 for a query language, much like PostgreSQL and MySQL, and supports an extended range of capabilities, including text search, time series analysis, location intelligence, and graph analytics.
Kinetica can operate on the entire data corpus by intelligently managing data across GPU memory, system memory, disk, SSD, HDFS, and cloud storage such as Amazon S3. With distributed parallel ingest capabilities, Kinetica can perform high-speed ingestion of streaming data sets (with Kafka) and complex analytics on streaming and historical data simultaneously. You can train TensorFlow models against data directly in Kinetica or import pre-trained TensorFlow or “black box” models to execute inferences.
Kinetica also has a GPU-accelerated library of geospatial functions to perform on-demand filtering, aggregation, time-series, geo-join, and geofence analysis. And it can display unlimited geometry, heatmaps, and contours, using server-side rendering technology (since client-side rendering of large data sets is extremely time-consuming). (Read .)
— Martin Heller