Kinetica Streaming Data Warehouse Update Designed for Petabyte-Scale Data Sets
Platform applies integration location, graph, and machine learning analytics to multiple data sources.
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Kinetica DB, Inc. has updated The Kinetica Streaming Data Warehouse, a unified data analytics platform providing real-time analysis of incoming data streams with location intelligence and machine learning predictive analytics. This release provides security, resiliency, and ad hoc analytics for petabyte-scale data sets.
The Kinetica Streaming Data Warehouse ingests and stores a wide variety of data types and analyzes the data in real time as it is received, which is a differentiator from traditional data warehouse technology. The new release is designed “for organizations trying to both incorporate high-velocity streaming data and glean the full business context of all of their data, taking into account factors like location, time, and interrelationships,” Irina Farooq, CPO at Kinetica, said.
The Kinetica Streaming Data Warehouse leverages CPU and NVIDIA GPU data processing for low latency and high performance. The platform is fully ANSI SQL 92-compliant and provides APIs to embed analytics in applications. It delivers results via any user interface, including BI and reporting tools, apps, and out-of-the-box data visualization functionality. By taking an API-first approach to building data-driven applications, The Kinetica Streaming Data Warehouse can present data at any point of user interaction, letting businesses use preferred tools, apps, and platforms across departments.
As a streaming data warehouse, Kinetica processes data in real time and unifies analysis of different data formats, such as relational, geospatial, graph, and time-series data at scale. Kinetica technology is developed with industry partners including NVIDIA-provided accelerated data analytics libraries and Oracle Cloud Infrastructure.
For details, visit kinetica.com.