Tableau and Kinetica’s GPU database together make self-service analytics easy, simple, and instantaneous. Now you can query small or massive multi-billion row datasets in seconds for discovering real-time, intelligent insights. Kinetica opens the way for Tableau to use GPU’s brute force compute and machine learning libraries such as TensorFlow, Caffe, and Torch along with Apache NiFi, Kafka, Spark and Spark Streaming, and Storm for fast OLAP, IoT, and streaming analytics. With Kinetica, Tableau business analysts and analytics pros can interactively explore and analyze billions of rows of data without long query wait times, timeouts, and tuning, enabling sophisticated big data discovery.
Attend this webinar to hear from Jen Underwood, a recognized analytics industry expert and Jonathan Greenberg of Kinetica, to explore the many ways Kinetica database can be combined with Tableau to accelerate the delivery of intelligent insights. Together they’ll discuss:
- Cost, performance, and ease-of-use benefits to adopting a GPU enhanced, in-memory speed layer for query and compute with Tableau
- Augmenting Tableau with machine learning and streaming analytics for richer insights
- How Kinetica’s in-memory, distributed, GPU database is mainstreaming the brute force compute power of GPUs offering 100x query performance improvement of Tableau reports and dashboards.
Jen Underwood, the founder of Impact Analytix, LLC, is a recognized analytics industry expert. She has a unique blend of product management, design and over 20 years of “hands-on” development of data warehouses, reporting, visualization and advanced analytics solutions. In addition to keeping a constant pulse on industry trends, she enjoys digging into oceans of data. Jen is honored to be a former Tableau Zen Master and active analytics community contributor. She also writes for InformationWeek, O’Reilly Media and other tech industry publications. Jen has a Bachelor of Business Administration – Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science – Data Mining from the University of California, San Diego.
Jonathan, Sr. Solutions Engineer with Kinetica, has spent the last three years with startup companies exploring modern and innovative analytic technologies and platforms. He revels in the pace of change in software and hardware for analytics, the introduction of the GPU enhanced database, and the business impact around convergence of ML and BI that Kinetica brings to this challenged big data (science) space. Prior to that, Jonathan worked for Cognos, BMW, and IBM, a twenty year career conceiving, developing, and delivering effective business intelligence solutions for a broad range of industries.