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Kinetica launches indatabase analytics via userdefined functions
Wednesday, January 25, 2017
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Christian Hargrave |
Kinetica has announced the availability of in-database analytics via user-defined functions (UDFs). This brand new capability makes the parallel processing power of the GPU accessible to custom analytics functions deployed within Kinetica. This opens the opportunity for machine learning/artificial intelligence libraries such as TensorFlow, BIDMach, Caffe, and Torch to run in-database alongside, and converged with, BI workloads. They have also introduced their flexible Reveal visualization framework for interactive, real-time data exploration.
Their in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one platform. It exposes advanced analytics to business users who understand the data resulting in better business value. By democratizing data science workloads, businesses get more efficient and effective business process outcomes, faster time to market, and net new business value.
- Execute and manage customizations for machine learning, AI and custom libraries.
- Leverage third party code by implementing orchestration hooks deployed via REST APIs to register/unregister.
- Call the endpoints via the exposed API and provide input table/output table.
- Use a true compute-to-grid fashion leveraging memory and GPUs without the need to move data. The output is stored in a Kinetica table for further analysis or analytics.
- Integrate user defined processes with a high speed IPC layer of every internal Kinetica data container.
- Use in native API bindings in C/C++ and Java, with Python support upcoming.
- Use arbitrary binaries to receive table data, do arbitrary computations, and save output to a global table in a distributed manner.
Additionally, Kinetica now includes Reveal for interactive real-time data discovery. With Reveal data exploration framework, business analysts can make speedy decisions by visualizing and interacting with billions of data elements instantly. Users do not need to know SQL and can simply drag and drop data tables to slice and dice data and start creating on-the-fly data analytics. Reveal has over a dozen analytical widgets to choose from for creating interactive real-time dashboards with just a few mouse clicks. It also features enhanced mapping capability and integrates with major mapping providers, including Google, ESRI, Mapbox, and Bing, to conduct interactive location-based analytics on massive datasets.
"In response to customer demand, we have combined the power of GPU-acceleration technology with UDFs, so customers can perform in-database advanced analytics and machine learning operations on massive datasets in real time, right alongside BI workloads," said Nima Negahban, CTO and co-founder, Kinetica. "This industry-first capability will enable customers to succeed and flourish in this new, exciting era of cognitive computing."
Their in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one platform. It exposes advanced analytics to business users who understand the data resulting in better business value. By democratizing data science workloads, businesses get more efficient and effective business process outcomes, faster time to market, and net new business value.
Key features include:
- Execute and manage customizations for machine learning, AI and custom libraries.
- Leverage third party code by implementing orchestration hooks deployed via REST APIs to register/unregister.
- Call the endpoints via the exposed API and provide input table/output table.
- Use a true compute-to-grid fashion leveraging memory and GPUs without the need to move data. The output is stored in a Kinetica table for further analysis or analytics.
- Integrate user defined processes with a high speed IPC layer of every internal Kinetica data container.
- Use in native API bindings in C/C++ and Java, with Python support upcoming.
- Use arbitrary binaries to receive table data, do arbitrary computations, and save output to a global table in a distributed manner.
Additionally, Kinetica now includes Reveal for interactive real-time data discovery. With Reveal data exploration framework, business analysts can make speedy decisions by visualizing and interacting with billions of data elements instantly. Users do not need to know SQL and can simply drag and drop data tables to slice and dice data and start creating on-the-fly data analytics. Reveal has over a dozen analytical widgets to choose from for creating interactive real-time dashboards with just a few mouse clicks. It also features enhanced mapping capability and integrates with major mapping providers, including Google, ESRI, Mapbox, and Bing, to conduct interactive location-based analytics on massive datasets.
"In response to customer demand, we have combined the power of GPU-acceleration technology with UDFs, so customers can perform in-database advanced analytics and machine learning operations on massive datasets in real time, right alongside BI workloads," said Nima Negahban, CTO and co-founder, Kinetica. "This industry-first capability will enable customers to succeed and flourish in this new, exciting era of cognitive computing."
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