So much raw compute power, you won't need to think about indexing, partitioning or downsampling!
Very Low Latency
With less to index, data is available immediately after it is written. No more waiting for data structures to be updated before the data can be returned in queries.
Ask any question
Since there is no need to prepare the schema before it can be explored, business analysts have complete flexibility and freedom for data discovery projects.
Linear Scale out
With simpler data structures, the GPU database scales in proportion to the size of the data. This leads to smaller and more predictable hardware costs.
Did we mention speed?
10x-100x faster than even the most advanced in-memory databases
Scale out on industry-standard hardware
The efficiencies of Kinetica's GPU architecture typically result in hardware costs that are 1⁄10 that of other in-memory databases.
As datasets grow, Kinetica's GPU database 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's GPU database sometimes requires just 1⁄10 of the hardware and 1⁄20 of the power when compared to other in-memory analytics solutions.
A Single Database for BI, AI, and Geospatial Workloads
Kinetica's GPU database provides a range of methods for data scientists, analysts, and line of business users to perform sophisticated analytics with large datasets on a single converged platform.
Full Text Search
Full text search functionality is supported by Natural Language Processing (NLP) Full result sets can be returned or rendered on a map. Results aren’t limited to the first 10, 20 or 30 pages of relevant information.
Text Search Capabilities »
User Defined Functions
UDFs offer an extensible and highly flexible framework for performing advanced analytics, predictive analytics and machine learning with data stored in Kinetica. UDFs have direct access to CUDA APIs, and can take full advantage of the distributed architecture of Kinetica.
In-Database Analytics »
Kinetica's GPU database has native support for geospatial objects (points, shapes, tracks) and comes with a suite of geospatial functions for filtering data by area, by track, custom shapes and more. Spatial operations are exponentially faster for massive datasets than traditional systems.
Location-Based Analytics »
Kinetica's OpenGL rendering pipeline is capable of drawing massive point datasets as WMS layers for use over maps. Kinetica can also generate heatmaps, scatterplots and even video. Native server-side rendering makes it possible to plot millions of points without bandwidth constraints.
Mapping & Visualization API »
Power and capability for developers and administrators
Ingest from multiple sources
Pre-built connectors for Apache Kafka, Apache NiFi, Apache Spark, Spark Streaming, Apache Storm, and ODBC/JDBC make it simple to ingest data from a wide range of data sources.
See Examples »
Kinetica provides linear scalability on industry standard hardware. Data replication is handled for high availability. Sharding can be done automatically, or specified and optimized by the user.
Advanced Analytics with In-Database Processing
Kinetica Reveal : In-Built BI & Visualization Tools
Bring your own BI dashboard, or take advantage of Kinetica 'Reveal' — a web-based visualization framework that makes it easy to slice, dice, and visualize data in Kinetica. Reveal works with Kinetica's native geospatial visualization pipeline to make it possible to see changes across large datasets as the underlying data or queries change.
- Allow users to easily and intuitively visualize datasets, and share interactive dashboards
- Visualize billions of data points on a map, filter and query them with real-time response.
- Take advantage of a wide gamut of geospatial visualization renderers such as feature, class break, heat maps, and geofencing.
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.