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webinar

Kinetica & Confluent: Optimizing Real-Time Analytics for Geospatial, Graph, and Machine Learning

MEET THE HOSTS: Rankesh Kumar, Partner Solutions Engineer, Confluent & Matt Hawkins, Director – Solutions Engineering, Kinetica

In anera where more and more data is streaming into the enterprise, there is a need to expand the techniques used to build smart analytical applications.  Innovative organizations require instant insight from streaming data in order to make real-time business decisions. There is also a growing need to consolidate and simplify the analytics platform so enterprises can easily incorporate a broad range of advanced analytical techniques including location intelligence, graph analytics, and machine learning analytics, without creating further component product sprawl. Kinetica’s distributed data ingestion ensures your data warehouse can scale with your Kafka infrastructure and provide a seamless analytical experience on constantly moving data. With distributed, in memory query capabilities, graph solvers, geospatial and a scalable machine learning deployment framework – Kinetica provides a fully integrated analytics experience in a single data warehouse.

IN THIS WEBINAR WE’LL EXPLAIN HOW:

  • Kinetica and Confluent provide advanced query capability that is performant while the data comes in. In other words, the capability to query as fast as you can stream.
  • Kinetica and Confluent can ingest multiple high velocity data feeds, run complex analytics, and then visualize, driving immediate insight and value.
  • Demonstrate how to track a large fleet of vehicles in real time and bring together relational, IOT, sensor and geospatial data to build event driven analytical pipelines with advanced OLAP, machine learning and graph solving techniques.

To learn more about Kinetica and Confluent, visit our partner page.

Watch the Recording