- Fuzzy Logix and Kinetica announced a partnership to release a joint solution allowing customers to leverage high performing advanced database analytics. The new option will target the most time sensitive and compute-heavy applications in healthcare, financial services, and retail.
The new tool combines Fuzzy Logix’s high performance data analytics and Kinetica’s GPU-accelerated database. The tool also offers significantly higher advanced analytics acceleration on 1/10th the hardware of CPU-only based solutions.
The central processing unit (CPU) is where most calculations take place and is often referred to as the brains of a computer. The graphics processing unit (GPU) is part of the CPU and was originally created for 3D game rendering, but its capabilities extend beyond image rendering. It was discovered that GPUs are more efficient than CPUs because they can process large blocks of data.
The new tool will extend Kinetica’s in-database analytic capabilities by hundreds of additional GPU accelerated machine learning and predictive analytics algorithms from Fuzzy Logix. The analytic functions will be able to utilize Kinetica’s distributed GPU pipeline through its User Defined Functions (UDFs).
“Leveraging GPUs for analytical workloads is on the rise, particularly among financial services, healthcare and retail organizations that often deal in extremely large data volumes with high scaling and real-time processing requirements,”451 Research Senior Analyst, Data Platforms and Analytics Jim Curtis said in a statement. “As such, the partnership between Kinetica and Fuzzy Logix should provide these additional analytical capabilities.”
The joint solution will have a library of algorithms on a SQL-compliant, in-memory database that leverages the GPUs parallelization and powerful real-time analytics capabilities. Use cases include computing portfolio risk management, options and equity pricing, product-based inventory optimization, next-likely purchase, prescribing habits of physicians and care gap analysis.
The joint tool is set to become available by the third quarter of 2017 and will be available as a premium offering from Kinetica. Customers will be able to get support from a single provider whether the solution is deployed on-premises, in the cloud, or in a hybrid environment.
“Faced with a data deluge, it’s essential for companies to look beyond CPU technology to discover what GPUs can deliver for extremely compute-intensive problems,” NVIDIA Vice President and General Manager Jim McHugh said in a statement. “Fuzzy Logix and Kinetica are building on each other’s strengths in accelerating time-to-market for a much-needed solution.”
GPUs have recently gained popularity in healthcare because of precision medicine and value-based care initiatives. The healthcare industry experiences one of the highest volumes of data for analytics over other industries. The high volume of data requires advanced processing power to handle and analyze the data in near real-time.
Real-time data has become more vital to healthcare organizations as patients and clinicians seek to more accurate information for diagnosing patients during their initial visit.
A survey conducted last year by OpsClarity stated that healthcare providers and life science companies are among the 92 percent of cross-industry organizations who plan to invest in near real-time big data analytics applications as soon as possible.
“The ability to harness the power of real-time data analysis gives businesses a competitive edge in today’s digital economy by enabling them to become more agile and rapidly innovative,” OpsClarity CEO and Co-Founder Dhruv Jain said in a statement.
“However, as the underlying stream processing data frameworks and applications are relatively new and heterogeneous in nature, end-to-end monitoring and visibility into these fast data applications is critical for organizations to accelerate development and reliably operate their business-critical functions.”
Fast streaming and near real-time analytics allow clinicians to make highly informed decisions while treating the patient. This cuts back on costs by reducing the amount of patient visits for the same condition.
Healthcare organizations are in need of faster processing power to offer near real-time analytics for patients and to offer quick and accurate diagnoses for patients.