In this video, Anand Venugopal, AVP and Business Head, StreamAnalytix, describes business and technical trends that led StreamAnalytix to develop a “multi-engine, open source-enabled, enterprise-grade platform” that enables businesses to analyze big and fast data. Anand outlines the benefits of adopting Apache Spark and StreamAnalytix for customers who want faster analysis, even if their current environment is primarily batch-oriented.
(Transcript below)
Adrian Bowles, RTInsights: We talked over a year ago about this marketplace for streaming analytics and how it fits with the growing open source market. Let’s talk a little bit about where you are today and what you’re seeing out in the marketplace.
Anand Venugopal, AVP, Impetus and Global Business Head for StreamAnalytix: StreamAnalytix is a product which attempts to enable the real-time enterprise and is successfully enabling the real-time enterprise.
Adrian Bowles: Okay. It sounds like, from what I’m seeing in the marketplace, you made a great decision in terms of timing, right? Everybody’s looking to go real-time, but a lot of people aren’t there yet. Right? They’re still doing batch but they know they want to do that. They know that at some point they want to go into streaming analytics. Tell me a little bit about what you’re seeing and how you’re going to help people wherever they are along that path.
Anand Venugopal: Yes, that’s a great question. We take customers from wherever they are in their data processing paradigm to being more real-time than they are. It could be the way the spectrum is from hours, days, and weeks to hours, to seconds and milliseconds, in terms of data processing, latency, and how quickly they can make decisions and act on them. Wherever they are in that spectrum, we can take them leaps ahead.
Adrian Bowles: Okay. Do you have any clients that you can talk about at the various stages where you’ve worked with them? Maybe somebody that’s really early that you take up a step or somebody that’s pretty far along.
Anand Venugopal: Yes.
Adrian Bowles: What industries?
Anand Venugopal: There are a number of use cases both in the telecom and financial services sector where people are beginning to do batch processing workloads on Spark. They’re already on Hadoop doing MapReduce jobs and they’re using Spark-based ETL approaches to speed that up and dramatically increase their efficiency and speed in data processing, so moving from Spark macro user, Hadoop MapReduce, to Spark batch jobs, we come in right there because StreamAnalytix now has Spark batch support.
Adrian Bowles: Okay.
Anand Venugopal: And people who are doing Spark batch and using Spark, and embrace Spark already are now coming into a few real-time use cases. Such as, hey, I want to detect my insider threat events much faster. I want to process my credit card reconciliation jobs much faster. I was doing it in 24 hours. I want to do it in two hours. I want to do it in 10 minutes. We’re taking them in that direction as well. So, and of course, there are hard-core real-time use cases, like industrial IoT, where you’ve got to react to an event from a machine, and within a few milliseconds, turn around a specific decision.
Adrian Bowles: Sure.
Anand Venugopal: And that’s a low-latency job, by definition itself.
Adrian Bowles: Okay.
Anand Venugopal: So we’re able to serve all of those use cases, and those are all real examples.
Adrian Bowles: I think that’s an important role to have in the marketplace today as people are looking at big data, as they’re trying to get more data points from devices that, you know, maybe weren’t instrumented in the past. So it looks like, when you started this, a while back, I don’t know if you had that full spectrum as part of your purview. But it sounds like you’re really able to broaden that and take advantage of Spark for example, which has gone from being one thing to being a much broader platform.
Anand Venugopal: Yes. Spark has now proved to be the big data and fast data processing platform. And StreamAnalytix is now enabling the complete unified fast data and big data processing and analytics for enterprises on Spark.
Adrian Bowles: Great.
Anand Venugopal: So we’re multi-engine, open source-enabled, enterprise-grade platform. For big data and fast data processing.
Adrian Bowles: I like that distinction. Big data and fast data, and fast is on a continuum. Alright. So you would say that your platform is open, built on top of, or integrated with a variety of open source.
Anand Venugopal: Yes.
Adrian Bowles: Good place to be. Well, thanks for the update. It sounds like a lot of progress in a short of time. I’d encourage people to find out more by going to the URL at the end of the video.
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