NVIDIA GPU Technology Conference – Kinetica, provider of the fastest GPU-accelerated database, today announced its GPU-accelerated, in-memory database is now available on Amazon Elastic Compute Cloud (Amazon EC2) P2 instances. Amazon EC2 P2 Instances are a new GPU instance type designed for compute-intensive applications. With P2 Instances, customers can now deploy Kinetica as a turn-key analytics database without upfront capital investments.
As customers embrace heavier GPU compute workloads such as accelerated analytics, artificial intelligence, high-performance computing, and big data processing, they need higher performance. P2 instances are powerful GPU instances available in the cloud, providing top-notch performance for compute-intensive workloads such as risk modeling, portfolio optimization, fraud detection, energy exploration and real-time route and inventory optimization. The P2 instances build on Amazon’s strengths of offering elastic, secure, and manageable solutions for faster time to value.
“Kinetica’s GPU-based architecture delivers unmatched performance and real-time analysis of massive data sets, particularly for use cases where time and location matter, whether on premise or in the cloud,” said Amit Vij, cofounder and CEO, Kinetica. “Amazon’s EC2 P2 instances deliver GPU-powered virtual machines in the cloud that complement our incredibly fast ingest and query response of well under a second on billions of rows of data. Today, our customers can now enjoy the same full enterprise database and visualization engine for exploring, analyzing, and visualizing multi-terabyte-scale data 10-100x faster than traditional in-memory data systems, with the reliability and scalability of Amazon EC2.”
Kinetica’s in-memory database accelerated by GPUs simultaneously ingests, explores, and visualizes streaming data on premise or in the cloud. It features:
- Scalability: Built from the ground up as a distributed database, Kinetica can be easily scaled, either horizontally or vertically, to meet requirements and SLAs for both data ingestion and accelerated analytics. The ability to pin data in GPU VRAM gives customers even better performance, while also still being able to leverage system RAM to both scale up and scale out to multi-terabyte in-memory processing.
- Security: Kinetica integrates with Open LDAP, Kerberos or Active Directory for authentication, supports RBAC (Role-Based Access Control) for database and table authorization, and supports encryption of data in motion and rest with SSL, PLS, and AES-256. Grant semantics make it easy to setup and manage user’s access to data.
- In-Database Analytics: A User Defined Functions (UDF) framework to run custom code and open source machine learning libraries such as TensorFlow and Caffe natively in the database with GPU-acceleration for machine learning, deep learning, and fast OLAP
- Location-Based Analytics: Native support for geospatial data, functions, and rich, map-based visualizations
- Advanced High Availability (HA). Kinetica offers Active/Active HA and automatic replication between clusters.
- Integration and Connectors Kinetica includes industry-standard connectors to make it easy to integrate with existing infrastructures. Kinetica’s APIs are fully supported in REST, Java, Python, C++, JavaScript and Node.js. Kinetica ships drivers for integration with industry-standard BI and SQL tools; features full SQL-92 query support through certified JDBC and ODBC connectors. Open source integration components include: Apache NiFi, Apache Spark and Spark Streaming, Apache Storm, Apache Kafka and Apache Hadoop. Also connect with common BI tools like Tableau, Kibana and Caravel.
