GPU-accelerated Database Analyzes Streaming Data with Enhanced Performance, Visualization, and High Availability

Kinetica today announced the newest release of its distributed, in-memory database accelerated by GPUs that simultaneously ingests, explores, and visualizes streaming data.  Version 6.0 features advances in performance, visualization and high availability for enterprise-grade stability and security.  Kinetica empowers organizations to perform a wide range of use cases in real time, including sentiment analysis, anomaly and fraud prevention, resource allocation on the fly, terrorist and other national security threat tracking, energy generation optimization, inventory tracking and customer engagement improvements.

Today’s common data management infrastructures are comprised of data warehouses and data lakes.  For companies looking to derive insights in real time from live data such as Internet of Things (IoT), gaps remain from systems designed to periodically load data in from transactional systems (OLTPs). Moving from CPU to GPU-powered databases allows businesses to actually ingest, explore, and analyze massive streaming datasets in real time, while simultaneously reducing their hardware footprint.

“Due to the rise in IoT, there’s a firehose of data streaming from every channel, device, and human interaction with a tremendous potential to act on that data,” said Nima Negahban, CTO and cofounder of Kinetica. “Kinetica uniquely addresses the new paradigm of data by accelerating the data center infrastructure with NVIDIA GPUs, merging the query needs of the traditional relational database developer with the scalability demands of the modern IoT-centric enterprise. Kinetica seamlessly plugs into existing data architectures and ingests, analyzes and visualizes in real time virtually all data sources via connectors including ODBC/JDBC, Apache Hadoop, Apache Kafka, Apache Spark, Apache NiFi, and others.”

New Features Added to Version 6.0 of Kinetica include:

• Even faster performance. More optimized and tuned.
• NVIDIA NVLink support. Accelerates database performance as data moves between GPU and CPU 3x faster on average compared to the traditional PCI Express.
• More advanced visualization. 3D acceleration with GPUs to deliver up to 5x faster visualizations.
• Advanced high availability for failover. No single point of failure with new intra-cluster failover protection, including native cluster resiliency. Even if a node fails, database will keep working,
• Full SQL-92 query support. GPU-accelerated SQL-92 query support through certified JDBC and ODBC connectors.
• Dynamic cluster resizing. Increase capacity of cluster more easily and more seamlessly by adding nodes while you are still online. Better cluster management for rebalancing workloads.
• New visual installer. Allows for easy click button installation of Kinetica across hundreds of nodes.

“Customers can accelerate their digital business solutions from 10-100x using Kinetica’s in-memory database accelerated by the immense compute power of NVIDIA GPUs,” said Jim McHugh, vice president and general manager at NVIDIA. “The performance and efficiency of Kinetica’s distributed solution greatly exceeds that of traditional in-memory databases, giving customers significantly faster time to insights, while reducing their infrastructure cost and footprint.”

"Competitive organizations that strive to win by leveraging big, fast data are now often hitting the performance and scale limits of today's in-memory databases and analytical tools -- especially when it comes to ingesting and querying tens if not hundreds of real-time data sources,” said Mike Matchett, senior analyst and consultant, Taneja Group. “Kinetica has created an exciting scale-out big data database that leverages a grid of GPU-powered servers that can ingest extreme amounts of data while simultaneously serving complex queries in fractions of the time of other big database solutions."

The primary benefits of the Kinetica database include:

Performance: Makes “real time” a reality by ingesting large petabyte-scale, streaming data, while delivering analytic results and producing visualizations in milliseconds.

Savings: Embedding GPUs into Kinetica’s architecture means there are 4,000-plus cores per device, versus 8 to 32 cores per CPU-based device. This translates into tangible savings from a smaller hardware footprint and less power and cooling.

Simplicity:  By plugging into existing data architectures, Kinetica seamlessly delivers processing and analytics without the typical tuning, indexing, or tweaking associated with traditional CPU-based solutions. Data consumption is also simplified via free-text search, a native visualization engine, and plug-ins with third-party business intelligence applications such as Tableau, Kibana and Caravel.

Native visualization including geospatial:  Kinetica provides complete geospatial object track type support and a native geospatial visualization rendering engine that, in tandem with its parallel processing analytics capabilities, make it the best modern geospatial information system (GIS) for use cases where time and location matter. It costs a fraction of legacy tools, can alleviate costly middleware, and delivers much faster performance.