GPU hardware acceleration is revolutionizing high performance computing. Kinetica's GPU database is redefining what is possible with big data.
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.
Scale Out with Less Hardware
As datasets grow, Kinetica is able to scale out across multiple nodes. Kinetica leverages the raw compute power of the GPU to break open processing bottlenecks and reduce reliance on indexes. In addition to performance benefits, customers experience dramatic efficiency gains. Kinetica sometimes requires just 1⁄10 of the hardware and 1⁄20 of the power when compared to other in-memory analytics solutions.
Kinetica's visualization tools render large volumes of data on the fly, and are particularly well suited for fast moving, location-based IoT data. You can also connect Kinetica to open source tools such as Kibana and Caravel, or to BI reports and dashboards via ODBC/JDBC.
Query Streaming data
Kinetica's multi-head ingest capability enables streaming data to be made available for query the moment it arrives. Kinetica plugs into existing data architectures and can connect to feeds using Apache Kafka, Apache Spark, Apache NiFi and commercial connectors.
SQL, REST API, and more...
Kinetica's SQL query capabilities enable business users to be immediately productive without learning new languages or systems. The native REST API and associated connectors provide data scientists a robust tools for interacting from Python, Node, Java, C++ and more.
Secure with High Availability
Enterprises rely on Kinetica's mature security model and high-availability architecture for mission-critical workloads. Multiple replicas of data eliminate single points of failure, and provide an eventually consistent data store with recovery capability.
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.