Projects Are Growing Out of the Hadoop Ecosystem

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "10 Biggest Big Data Trends."

So these types of projects are growing out of the Hadoop ecosystem. Our partners like Cloudera and Hortonworks are really driving a lot of the work behind these changes. Ultimately they are adding what we will call enterprise grade capabilities that you would expect from your traditional relational database platforms like Oracle or IBM and an SQL Server.

So these capabilities are moving into the forefront of emerging Big Data technologies, and they are really eliminating another one of those barriers to true enterprise adoption and that the states moving really, really quickly. Holly, is there anything you are seeing around this that you think would be interesting for the audience to hear?

Effort the Data Platform Vendors

Holly: So I am encouraged by the effort the data platform vendors, the Hadoop vendors and the Big Data vendors are putting into this because I can say that I have worked with a lot of customers helping them with governance and security specifically for their Big Data platforms.

And it's encouraging to see that we are able to support this requirement because governance and security for me for business intelligence is actually an enabler. It's not a barrier to entry to getting value out of the data. Sometimes we think governance and security is keeping people out.

But by supporting the enterprise requirements for governance and security, we actually open up whole new use cases and enable a whole new area of analytics. If you think about financial services and healthcare where compliance and security is critical, it's critical across the board, but by meeting the needs of the enterprise for governance and security, we are creating whole new opportunities and enabling a lot of great use cases. We are starting to see that and we have seen that in the past with InetSoft, and it's great to see the industry as a whole take it so seriously.

Abhishek: I have question here that is a perfect segue on to our next trend which is how to keep a good balance on the data governance effort when enabling self-service for Big Data. In our last trend, there is a technology area that we talked about that that facilitates this really well, and it's the rise of metadata catalogs that help people find analyses for Big Data, and it can also really be used to facilitate a flexible form of governance.

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Organizing Hadoop Data

Larry: Yeah, it's a great segue. So we talked about Hadoop adding to enterprise standards, and part of those standards have to do with how governed your information is. So to give you a little bit of context, for long time companies threw away data because they had too much to process.

With Hadoop they can process lots of data, but the data isn't generally organized in a way that can be found, and metadata catalogs are answering that. They are helping users discover and understand analysis-worthy data using self-service tools.

One technology that I particularly like to highlight is Alation. They came out with the InetSoft application edition last November, and they are just essentially surfacing the necessary context around the data so broad groups of people can collaborate and discover insights with the appropriate controls, which of course, in turn, enables governed self-service analytics.

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Best Form of Governance

So to me, the best form of governance is when you're governing the data, but at the same time users don't feel they are governed. They are actually empowered. They find the right information when they want it, and they are not encumbered in anyway. That's exactly what Alation is trying to do. I know

Abhishek, you participated in a webinar with a customer using InetSoft and Alation.

Abhishek: Yeah, that should be available on the on-demand webinar section of InetSoft.com if people want to hear that story, but it's a good use case of exactly how an organization is enabling several thousand users of InetSoft to do self-service around governed data sets.