InetSoft Webinar: Can Hadoop be the end all for big data analysis in the future?

This is the continuation of the transcript of a Webinar hosted by InetSoft in April 2018 on the topic of "Data Discovery Tools and End User Mashup" The speaker is Abhishek Gupta, product manager at InetSoft.

So that’s the presentation. We’ve got about six minutes left over to do some questions. I think we have a number of them here. Oh, I just want to mention some recommended research here that says how to deliver self-service BI notes. The first one list is probably the main one I would take a look at. Alright, as far as questions, just give me a second to look through these quickly.

Can Hadoop be the end all for big data analysis in the future? That’s one of those crystal ball kind of questions. There is no question I think right now that Hadoop is -- I mean Big Data itself as a term and a lot of people equate Hadoop as Big Data is the #1 search term by far in BI. So there is a huge amount of interest here.

When I look at vendors that do data discovery on Hadoop based data, I don’t see doing it comparably to BI vendors like us. They are dependent upon Hadoop becoming more widely deployed. I think right now, a lot of people are using Hadoop for cheap storage if you have got petabytes of data. If you need cheap storage, you are going to use Hadoop for that.

View a 2-minute demonstration of InetSoft's easy, agile, and robust BI software.

Now inevitably you are not going to use that for just storage, we are going to use that for analysis. So my answer to this is I think Hadoop is here to stay. There is no question. I think likely Hadoop is here to stay, and this idea of an open source distributed file system to handle large amounts of data and a wide variety of data that doesn’t have to be modeled.

I would object to the term be all and end all in the sense that I think there will always be lots of different data sources that are not Hadoop and the point of data discovery is to blend different data sets together whether they are in Hadoop or other SQL or screening or other types of information sources. And I think data discovery is going to need to sort of handle that type of variety of source so to speak.

Any comment on machine learning BI apps? I mentioned that with smart pattern data discovery, and there is this notion that machine learning is going to play a bigger role here. I didn’t go in lot of detail on that because I don’t think that was the focus of this particular presentation.

How would self-service BI be different across different media like say mobile? I don’t think it would actually be that different. I think a lot of mobile right now, because remember that one slide I showed that showed information consumption versus information creation? A lot of information delivery via mobile is just a different way to consume the data, whether it’s a mobile app, whether it’s an HTML5 browser running on your iPad, it's going to be a dashboard that we are going to consume.

So I don’t think mobile changes the self-service BI strategy that much. Hey, we do have a few more questions, I apologize that I am way out of time. I don’t think I am going to be able to address these on the call right now. We’ll follow up with them offline. And remember if you're considering evaluating our BI app, please visit inetsoft.com and click on Evaluate. Thanks again everyone. Have a good day.