The GPU parallelized processing architecture not only enables near-linear scalability, it also reduces analytical processing times for multi-billion row data sets by more than 100x compared to leading in-memory and analytical databases.
Artificial Intelligence and Business Intelligence together...
on a GPU-Accelerated Database
GPUs are well suited for the types of vector and matrix operations found in machine learning and deep learning. In-database processing on Kinetica opens the way for machine learning and artificial intelligence libraries such as TensorFlow, BIDMach, Caffe, Torch and others to work directly on data within Kinetica.
Now AI workloads can run together on the same GPU-accelerated database platform as BI operations. Doing so makes it possible to quickly deploy new models and eliminates the time and effort required to transform data and move it back and forth between a database and a separate data science system.
Ideal for Location-Based Analytics
Kinetica comes with a native geospatial and visualization pipeline; for rendering large volumes of data over maps. This makes Kinetica particularly well suited for fast moving, location-based IoT data. Kinetica includes Reveal – an extensible and flexible web-based analytics visualization framework. You can also connect Kinetica to other BI tools such as Tableau and MicroStrategy via ODBC/JDBC.
Distributed In-Memory Architecture
Kinetica is a distributed, columnar, relational database designed for analytics on large and streaming datasets. Tiered memory management allows data to be held in VRAM and system memory, with persistence to disk.
Take the Kinetica Challenge!
Sometimes benchmarks and marketing copy can sound too good to be true. The best way to appreciate the possibilities that GPU acceleration brings to large-scale analytics is to try it with your own data, your own schemas and your own queries.
Contact us, and we'll set you up with a trial environment for you to experience it for yourself.