Advanced In-Database Analytics
...Powered by GPUs

User Defined Functions (UDFs) enable AI to run alongside BI and other advanced analytics on the same data platform.

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.

Why in-database analytics is important »

Kinetica UDFs

Example: User-Defined Functions to Predict Car Sales

In this demo, a linear regression is applied in-database to historical car sales data, to predict future sales.

Benefits

An extensible and highly flexible framework for getting the most out of data stored in Kinetica.

Advanced Analytics

UDFs can include advanced analytics computations such as linear interpolation, anomaly detection, clustering, regressions, or risk simulations such as Monte Carlo analysis.

Machine Learning

Custom functions can also call machine learning/artificial intelligence libraries such as TensorFlow, BIDMach, Caffe, Torch and others to work directly on data within Kinetica.
How Kinetica fits in the ML Stack »

ExtensibILITY

Pre-existing custom code that currently operates in separate systems can often be quickly reconfigured to run in Kinetica. UDFs open up a world of options for automating processes and performing business calculations within the analytics platform.

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.

Bringing AI to BI : Observations from the Field »

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.

Advanced In-Database Analytics ...Powered by GPUs