MEET THE HOSTS: Amit Vij, President & Co-Founder, Kinetica & Nima Negahban, CTO & Co-Founder, Kinetica
In recent years, “Big Data” has evolved to meet the rising demand of streaming data, cloud data warehousing, graph analytics, location intelligence and machine learning. And as a result, the world of Hadoop has grown overly complex, with bolt-on components to satisfy each of these new requirements and analytic techniques. Today, more and more data is moving to the cloud, in an effort to simplify and consolidate the data ecosystem. Object stores from the cloud platform players are increasingly replacing HDFS for its cost effectiveness over HDFS. However both object stores and HDFS have a poor price-performance ratio when it comes to complex analytics. There is a clear opportunity for a solution that can complement or replace components of the Hadoop ecosystem that simplifies complex architectures, is highly performant, cost effective, supports complex workloads, and delivers the continuous results organizations now require.
IN THIS TALK WE WILL DISCUSS:
- How to simplify the messy architectures of the Hadoop world
- How to leverage Kinetica with Kafka and Spark as connectors to HDFS to enable real-time analytics at scale
- Solving complex, high-velocity, and high-scale use cases without bolt-on solutions for Hadoop
- How keeping data at rest and conducting all analytics in one technology results in orders of magnitude better performance than the Hadoop stack
- Real-world use cases