Kinetica, provider of the fastest GPU-accelerated database, today announced its in-memory analytics database is immediately available on Microsoft’s N-Series of Azure Virtual Machines with GPU capabilities. With Azure NC-based instances, customers can now simplify their on-ramp to the cloud and deploy Kinetica with the cost advantages of Azure’s on-demand cloud services.
As streaming analytics, machine learning, deep learning, and accelerated analytics become more ubiquitous, higher performance and elasticity is needed to accelerate compute workloads. Azure NC-based instances are powered by NVIDIA GPUs, allowing customers to use these instances to run deep learning models, HPC simulations, visualizations, real-time data analytics, and many more GPU-accelerated database tasks. Customers can now run Kinetica on Azure for time-sensitive, compute-intensive data and analytics use cases such as credit risk management, drug discovery and development, and supply chain optimization.
Brett Tanzer, Product Owner for Big Compute, Microsoft Corp. said, “GPU acceleration in the cloud has immense value to end customers. As more end users are capitalizing on IoT use cases focused on data originating in the cloud, it is important that they provide analytics in real-time. Kinetica’s advanced 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.”
“Cloud computing is completely changing the way businesses operate, and to derive real-time insights from data in the cloud, you need the right high-performance database solution coupled with powerful applications,” said Chris Prendergast, Vice President, Business Development and Alliances, Kinetica. “Azure NC-based instances deliver GPU-powered virtual machines in the cloud that complement Kinetica’s lightning-fast speed, so data can be accessed in mere milliseconds, versus tens of seconds with normal queries on CPU bound technologies. With Kinetica’s accelerated visualization capabilities that can render large volumes of data on-the-fly, Kinetica is particularly well suited for fast moving, location-based IoT data. Customers can plot billions of data points and see changes in real-time as the underlying data or queries change.”
Kinetica’s in-memory database accelerated by GPUs simultaneously ingests, explores, and visualizes streaming and historical data for advanced analytical processing. It features:
- Simplicity: A SQL-92 query compliant, columnar, relational database with certified ODBC/JDBC connectivity and connectors for Hadoop, databases, BI and ETL tools and streaming solutions such as Apache Kafka and Apache NiFi.
- Performance: 10-100X performance improvements with 1/10th the hardware. GPU-accelerated, in-memory, distributed database for compute, throughput, and scale.
- In-Database Analytics: User Defined Functions (UDFs) framework to run custom code or open-source machine learning and deep learning libraries such as TensorFlow and Caffe natively in the database. Native support for OLAP and geospatial analytics.
- Native GPU-accelerated visualizations: GPU-accelerated, distributed visualization framework to quickly visualize billions of data points
- Tiered Data Management: In-memory data management in GPU VRAM and system memory with persistence on disk to scale up and out to multi-terabyte data sets
- Enterprise-grade: Enterprise grade security, availability, and reliability. Security features include – open LDAP, Kerberos, and Active Directory for authentication, Role-based access control for authorization, and encryption of data in motion and rest with SSL, PLS, and AES-256. Comprehensive high availability support with replication, and 5-9’s up time and no data loss delivered.
