Impetus Workload Migration Solution, StreamAnalytix and OLAP on Big Data solution

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In this video from the DataWorks Summit / Hadoop Summit 2017 conference in San Jose (June 13-15, 2017), insideAI News’s Managing Editor and resident data scientist Daniel D. Gutierrez chats with Larry Pearson, VP Marketing of Impetus Technologies, Vineet Tyagi, CTO of Impetus Technologies, and Ajay Anand, VP Products of Kyvos Insights. Discussion topics include: an overview of Impetus and the problems its products solve, an overview of the Kyvos Insights BI solution offering “OLAP on Big Data,” and also how Impetus approaches data lakes.

Ajay Anand offers some insights into how his company’s solutions were being accepted at the DataWorks Summit:

The response has been great and that’s primarily because we solve a core problem for most customers that are moving to big data. The first thing people do when they move data into a data lake is [determining] how do you expose this to business users so you can start getting return on investment for this big data strategy. What we do is eliminate the barrier for the business user to really get access fast from this data and get immediate response time using the BI tools that they’re already comfortable with such Tableau or MicroStrategy or any other tool. They can continue to use their existing tools but now have all this data available to them with instant response. Trying to do this with traditional SQL and Hadoop has not proven to work out because it takes a long time to run SQL queries. With us, we eliminate that barrier.”

Impetus has been working to migrate customers from the enterprise data warehouses over to Big Data lakes for the last ten years. Through doing 20-plus large migrations, the company has realized there is a repeatable framework that can be used. Vineet Tyagi provides an overview of the messaging his company promoted during the DataWorks Summit, specifically how it relates to data lakes:

We have realized that the true power of big data is in advanced analytics and advanced analytics. It’s all about machine learning bringing in deep learning artificial intelligence to drive the business value and that requires a ton of data. The challenge is moving data locked up in the silos in the enterprise data warehouses into the data lake. That problem has been solved. We do that automatically. There are other tools however there is a very big value that has been locked into the enterprise data warehouses and that is the analytical workloads that have been created over the last 20 years that is your business logic. Your business value that has been built around the data that enterprises have been acquiring and the challenge remains as to how to take and unlock the analytical workloads and make them work on the big data. We have solved that challenge. Right now the challenges that enterprises are facing in unlocking the value with the workloads are they typically don’t have the expertise and the skill sets which are required to do these manual migrations which can be huge.”


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