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Data Modeling in the Big Data Age: A Q&A with Neil Buchwalter of ERwin

Originally published April 24, 2014

This BeyeNETWORK article features Ron Powell’s interview with Neil Buchwalter, senior principal product manager for ERwin. Neil and Ron discuss how today's enterprises are utilizing ERwin for data modeling in the age of big data.

Our audience is very focused on data modeling and data management. So to begin our interview, I'd like to ask you what your customers are looking for from a tactical perspective for data modeling with ERwin.

Neil Buchwalter: Customers are looking for specific core functionality including forward engineering, reverse engineering, schema generation, complete compare, and the ability to look at models and databases that they have and compare them to find the differences so that they can make changes and manage their database structures in a very well-oiled and directed manner.

Neil, you have been in data modeling for quite some time. Can you give us a little background on your experience?

Neil Buchwalter:
My background started back in the days when data models were drawn by hand and it was very labor-intensive, and there were not a whole lot of mechanics behind it. Essentially, data models were pretty pictures that defined structures, but there wasn't much more to it than that. I was around when some of the early data modeling tools like Excelerator, the IEW from KnowledgeWare and even IEF were first being developed. That's when data modeling and modeling in general got really interesting because you put some really strong rules behind what was happening so you couldn't just draw a pretty picture and say, "Hey, this is what a data model is and this is what my database is going to look like," but now there was rhyme and reason, there were rules that ensured that what you did was correct and valid as opposed to being just a pretty picture. You then had the ability to take these pictures and evolve them from one perspective being the business or logical perspective into a physical database or an implementation perspective through the use of the tool components so that you could then automatically generate the DDL (data definition language) required to actually define and build your databases for you.

My background goes from the beginning right through to where we are today.

Let's go with where we are today. How are your customers utilizing ERwin and data modeling as an integral part of what they do?

Neil Buchwalter: A company that knows what data modeling is knows the value that it brings to the table in terms of ensuring that their databases can support the business and help their business be successful. In fact, not only be successful, but compete successfully with their competition. So a data model is going to help them build their databases, structure their organization, build their data warehouses, and provide the information required to run that business in a very positive and successful manner.

What our customers are looking for is an easy way to create those models, to share those models with others within the organization, to communicate that information to collaborate with other stakeholders across the organization – like business analysts, developers and others – and then be able to generate all those databases that are necessary that are going to maintain the information required.

What do they look for? Easy-to-use pictures that are incredibly easy to communicate with others and just the ability to ensure that the databases and the structures they build are valid and useful.

You mentioned, obviously, databases. Over the last several years we have other data sources that are coming into play especially around big data. How is ERwin adapting to what's happening with big data from a modeling perspective?

Neil Buchwalter: As you said, big data is something that has become quite prevalent in the market over the last couple of years, and what we've done in our latest release of ERwin Data Modeler is introduce a set of metadata exchange bridges that allow us to actually go out to big data sources that are managed primarily in Apache Hadoop Hive as well as in Google BigQuery and bring that information into ERwin. One of the downsides, if you will, of big data is that although it allows you to manage structured and unstructured data very easily, understanding what you have collected in these big data environments is very difficult, and it takes some very specific expertise to actually work in those environments. So what we've done in ERwin is we've built these metadata bridges that allow you to look into those environments, understand what's out there, bring it back into the more traditional data modeling environment and actually create pictures of those big data environments. In essence, we can document what you've got, and then once you've documented that information, you can then begin to utilize it from a data modeling perspective or an architecture perspective in other areas of the business. For instance, I can now take my big data information that's relevant and move it into my data warehouse or integrate it with by business analytics and BI environments, and so on.

So Neil, from a best practices perspective, a lot of people are just getting started with big data. What best practices would you give them as advice in moving forward in this area?

Neil Buchwalter: Well, from a best practices perspective, working with big data is a very daunting type of activity because there is so much data and so much of what you can collect may or may not be relevant. So, from a best practices perspective, you need to have the right kind of people that understand what the business is all about, understand IT as well, and put them in a position whereby they are not only collecting the information, but they are mining the information and understanding what's there. We talk about big data like it is the be-all and end-all, but at the end of the day, it's really nothing more than a lot of information, some of which is relevant and some of which is not. So once it's been collected, you need these people – and this is a new role in the organization – referred to as data scientists. You need people that can really look at that information from a business perspective and from an IT perspective to go out there based on a premise – whatever that premise may be – and understand that information and then mine through all of the information to find the valid, legitimate information that can be used across the organization to help that organization again be successful in their marketplace. In fact, to be more successful than they already are.

One last question, Neil. Obviously the new ERwin release is out. What kind of feedback are you getting from your customers with regard to the new release?

Neil Buchwalter: We're getting a lot of very positive feedback, not only about the big data metadata bridges, the metadata bridges that we've included, but also about a reintroduction of sorts of the new reporting capabilities within the product. We now have an easy-to-use, essentially point-and-click capability for generating metadata reports about anything that is managed within the ERwin environment, and customers really like this new reporting capability.

Great, as always Neil, it has been a pleasure talking with you about ERwin and we're looking forward to future releases.

  • Ron PowellRon Powell
    Ron is an independent analyst, consultant and editorial expert with extensive knowledge and experience in business intelligence, big data, analytics and data warehousing. Currently president of Powell Interactive Media, which specializes in consulting and podcast services, he is also Executive Producer of The World Transformed Fast Forward series. In 2004, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). He maintains an expert channel and blog on the BeyeNETWORK and may be contacted by email at rpowell@powellinteractivemedia.com. 

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel.

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