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Kinetica provides GPU acceleration to supercharge data analytics with SQL to Tableau

 

Kinetica, provider of the fastest GPU-accelerated database, announced on Monday availability of native integration with Tableau allowing users to simultaneously ingest, explore, analyze, and visualize data within milliseconds to make critical decisions and find efficiencies, lower cost, generate new revenue, and improve customer experience.

Tableau combined with Kinetica helps businesses bring AI and BI together; deploy location-based analytics; perform streaming analytics with single-click Tableau integration.

Kinetica’s open architecture features a User-Defined Functions (UDFs) framework to extend database functionality, enabling developers and data scientists to deploy custom code, open source, and advanced machine learning libraries natively within the database as GPU-accelerated business logic to power advanced business analytics.

Kinetica natively manages geospatial data such as points, shapes, tracks, and labels and provides out-of-the-box functions for location-based analytics. Kinetica’s “Reveal” visualization framework enables interactive real-time data exploration in conjunction with GPU-accelerated rendering of maps and accompanying dashboards. With Kinetica and Tableau, business analysts can make faster decisions by visualizing and interacting with billions of data elements instantly.

Kinetica’s in-memory database is designed to take advantage of the parallel processing nature of the GPU for streaming analytics on large, complex real-time data from sensors, connected devices, social media, and mobile apps. Kinetica features connectors for Apache Kafka, Apache Nifi, Apache Storm, and Apache Spark and ingests large, complex data in parallel making streaming data available for query and analytics in real-time on Tableau. Tableau’s Replace Data Source feature ensures users can take advantage of Kinetica’s speed and advanced analytics without any changes to existing Tableau workbooks. Point Tableau workbooks to Kinetica and leverage Kinetica’s ODBC and JDBC connectivity along with SQL-92 support to accelerate Tableau workbooks.

Kinetica’s native GPU-based parallel processing removes the need for expensive, difficult, and resource-intensive database tuning, indexing, aggregation, and data marts and delivers 100X query performance improvements on commodity hardware. That’s supercharging the Tableau workbooks with ideal price/performance ratio.

Kinetica’s database runs completely in-memory to optimize throughput and deliver fast query performance. A tiered memory management approach ensures that hot, warm, and cold data can be distributed across GPU VRAM and system memory to balance capacity and performance. A column-oriented database design ensures that the data structures are optimized for in-memory management and fast analytics. A relational data- base model with familiar concepts such as tables, columns, and SQL support ensures that Kinetica is easy to deploy, use, and manage.

A Kinetica cluster contains data sharded across multiple nodes to leverage parallelization for ingest, analytics, and visualization. Additional nodes can be added for scale-out to improve query performance and system capacity. Kinetica can power real-time Tableau queries, reports, and dashboards by simultaneously ingesting data into the same tables that are being queried by Tableau.

Kinetica features multi-head, multi-thread, parallel ingest to capture and provision millions of rows of data in seconds. Minimize data preparation, tuning, and aggregate data on the y to ingest, provision, analyze, and visualize millions of rows of data in Tableau within seconds. Kinetica leverages the massive parallelization and brute-force compute of GPUs to minimize aggregate tables, pre-computed calculated columns, and cubes simplifying the analytics pipe- line. With Kinetica, Tableau business analysts are free to interactively explore billions of rows of data without limitations and discover hard to find insights.

Kinetica’s in-memory database is designed to take advantage of the parallel processing nature of the GPU for streaming analytics on large, complex real-time data from sensors, connected devices, social media, and mobile apps. Kinetica features connectors for Apache Kafka, Apache Nifi, Apache Storm, and Apache Spark and ingests large, complex data in parallel making streaming data available for query and analytics in real-time.

Kinetica is designed for seamless integration BI and ETL tools, and business applications. Kinetica supports industry standards such as REST APIs, ODBC/JDBC, SQL, and connectors for certified integration. Support for commodity hardware from IBM, Dell, HP, Cisco, and NVIDIA and cloud providers such as Amazon Web Services, Microsoft Azure, Nimbix, and Google Cloud Platform ensures that Kinetica can be deployed on premise, in a public cloud, private cloud and hybrid.

Kinetica is designed for enterprise-grade security, reliability, availability, and scalability. Authentication and authorization features such as LDAP, user/group/role based access control ensure that data is always secure. Kinetica supports high availability with inter-cluster active-active configuration and data replication. A distributed, scale-out architecture with tiered memory management across VRAM, system memory, and disk ensures scalability.

“We are excited for Kinetica to join our partner program,” said Todd Talkington, Director Tech Partners at Tableau. “GPUs are designed around thousands of small, efficient cores that are well suited to performing repeated similar instructions in parallel. This makes them well-suited for compute-intensive analytics workloads on large data sets.”

“Tableau users will experience productivity improvements and deeper, advanced analytics on more sophisticated workloads as they incorporate an in-memory analytics database leveraging GPU-acceleration for lightning fast query performance,” said Chris Prendergast, VP of Business Development and Alliances, Kinetica. “Together, Tableau and Kinetica democratize data science by making advanced algorithms available to business users.”

This article was originally published on wwpi.com and can be viewed in full

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