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Kinetica

Kinetica

harnesses the power of GPUs for unprecedented performance to ingest, explore and visualize data in motion and at rest. More »

 

Kinetica

 

 About Kinetica
2012 2009
Deployed into US Army Intelligence Command Solution found in parallel processing with GPUs
After extensive research and development, the new database, known as GPUdb, was launched at global scale, ingesting and analyzing over 200 different data feeds, to track terrorist activity. After extensive testing and research revealed no existing systems capable of meeting the Army’s needs, Amit Vij and Nima Negahban built from the ground-up a new database, centered around massive parallelization utilizing the GPU, to explore and visualize data in space and time–an approach that has been patented.
2016 2015
New Brand, Success in the Enterprise Commercial Entry
Company re-branded to Kinetica. As version 5.2 is released, Kinetica receives its second High Performance Computing Innovation Excellence Award from IDC. Former President & COO of Oracle, Ray Lane, provides initial seed funding. Kinetica opens offices in San Francisco. With the growth of data from IoT, transactions and other sources, businesses users started to run up against the challenge of streaming and analyzing data in truly real time. USPS deployed GPUdb into production to optimize routes and increase accuracy.
2017 2017
Growth & Expansion Bringing AI to BI
Kinetica closes a $50-million Series A financing jointly led by Canvas Ventures and Meritech Capital Partners with participation from Citi Ventures and existing investor Ray Lane of GreatPoint Ventures Kinetica v6 is released with improved SQL support, and in-database processing capabilities which positions Kinetica as the ideal database for deep learning and machine learning on large and streaming datasets. Kinetica gains traction with more enterprise customers as executive team grows.

How it Works, Bring the model to the data, not the data to the model
User-defined functions (UDFs) enable GPU-accelerated data science logic to power advanced business analytics, on a single database platform. UDFs have direct access to CUDA APIs, and can take full advantage of the distributed architecture of Kinetica. Because Kinetica is designed from the ground up to utilize the GPU, users have an advanced set of tools for distributed computation.
UDFs are able to receive filtered data, do arbitrary computations, and then save output to a separate table. The brute-force parallel compute power of the GPU delivers fast response which makes it highly valuable for interactive analytics and experimentation.
GPUs are also particularly well suited for the types of vector and matrix operations found in machine learning and deep learning systems.
Democratize Data Science
Deploy and test data science models on the same database platform as is used for business analytics. No need to export data to specialized high-performance computing (HPC) systems staffed by data scientists. With in-database processing on Kinetica, BI and AI workloads can run together on the same GPU-accelerated platform.
 
Business users can be empowered to do more sophisticated analysis without resorting to code. Data science teams can develop and test gold-standard simulations and algorithms while making them directly available on the systems used by end users. Foreseeably, in addition to query, reporting and analytics, users could also call a Monte Carlo simulation, or other custom algorithms, straight from their BI dashboard.