What advice do you have for others considering erwin Data Modeler by Quest?

I recommend it for those doing strictly data modelling. I can't give a recommendation for enterprise architecture as I haven't used it enough. My overall rating is eight out of ten.

Reverse engineering is a very powerful feature. I have used it many times when working with clients, which has helped solve implementation issues. Erwin utilizes artificial intelligence to automatically create definitions and generate logical or physical data models tailored to specific industries. I recommend the solution. Overall, I rate the solution a ten out of ten.

I am using the latest version of erwin Data Modeler by Quest. I recently built a data model for integration with another software product that we're going to purchase. I did it really fast with erwin Data Modeler by Quest. If I didn't have the solution, I couldn't have done that, and I couldn't have shared the results. I would recommend the solution to other users. Overall, I rate erwin Data Modeler by Quest ten out of ten.

The amount of data the tool works with is minimal, so scalability is irrelevant to the tool. It just uses metadata. People looking to use the solution must compare it with other tools like ER/Studio. ER/Studio and erwin Data Modeler are quite similar in the functionality they offer. It all comes down to what works for us in terms of pricing. Overall, I rate the solution an eight out of ten.

It's a good enough solution if the whole purpose of your data modeling is to generate databases. I also use data modeling for other purposes, and that's why I prefer Visual Paradigm. It allows me to do data modeling that's not so much focused on databases. Overall, I rate erwin Data Modeler by Quest a seven out of ten.

The solution is on-prem, but it can be on the cloud, and it has .NET SQL, which is part of the reason we chose erwin. There's a possibility we'll need .NET SQL as well. To anyone evaluating erwin DM, I advise you to watch the videos and read the documentation. I rate the solution an eight out of ten.

I would rate the erwin Data Modeler an eight out of ten. They have small videos that explain how to use the tool and they are very useful. If you know the principles of data modeling, erwin will be very easy to learn. If you are new to data modeling, then I’d recommend getting a course on data modeling first.

I recommend erwin Data Modeller because it is a good data modeling solution in comparison to others available in the market. I rate the overall solution an eight out of ten.

SAP solution is more stable compared to Data Modeler and fits our requirements as an enterprise team. I rate the overall solution a seven out of ten.

I rate the solution nine out of ten. My advice to those considering the solution is to use dedicated data architects; when you give this type of product to development teams, there can be issues around creating and following standards, which is essential for data model integration. You don't want different teams defining the same types of columns with varying lengths, like dollar amounts. If the entire company agrees that the dollar amount field is 18 digits long and two digits to the right of the decimal place, then you're consistent. If different teams disagree, data can't be transferred from one database to another without truncation. Having a centralized team that enforces standards is critical.

The solution is the best option in the market. I rate it a seven out of ten.

My advice to those considering this solution would be that they should first evaluate what they need. I suggest they maybe do a POC to evaluate their use cases and then work at finding a solution, and run all the necessary tests before starting to work with the solution. I would rate this solution as a seven out of ten.

I would rate erwin Data Modeler (DM) a nine out of ten.

It is a very good product if you want to import existing designs. It is a tremendously flexible product for reverse engineering and database generation. I would rate it an eight out of 10. The main reason is that it has lots of features. It is extremely flexible, but some of the areas need a bit more debugging, testing, and fixing.

I rate this solution as nine out of 10.

Oracle Data Modeler, which is free, is one of the competitors that erwin has. You can't argue with the price point on that one, but erwin is much more comprehensive and easier to use. It is easier to display information and models to business people than something like Oracle Data Modeler, which does the job, but erwin does it a lot better. So, my advice would be that if you can afford it, get it. Its visual data models have certainly improved over time in terms of overcoming data source complexity and enabling understanding and collaboration around maintenance and usage. It was originally designed as a tool to build databases with, and it retains a lot of that. It still looks like that in a lot of cases, but it has also been made more business-friendly with a sort of new front end. So, it used to be all or nothing where when you wanted to show somebody just the entity names or just the entity descriptions, you had to switch all of the entities on your diagram just to show names. Now, you can show some of them. You can shrink down some of them, and you can keep some of them expanded. So, it has become a more useful information-sharing tool over time. It is extremely helpful. In my previous company, it was the enterprise data model, and you could paper a room with it if you printed the information out. To present that information to people, we had to chunk it down into subject areas. We had to present smaller amounts of information. Because it was linked to the underlying system, we could reuse the information that we had in a model in other models. The biggest lesson was to chunk the information down and present it in a digestible form rather than trying to show the entire thing because otherwise, people would run away screaming. One of the places didn't have a modeling tool in it, and they were trying to do the documentation using Confluence. It was just a nightmare trying to keep it maintained with different developers using different tables and then needing to throw something into one and adding something into another one. It was just a nightmare. If they had one tool where they could put it all in one place, it would have been so much easier than the mess they had. I would rate erwin Data Modeler a nine out of 10.

You really need to sit down and consider how you want to organize your models, and how you should set up security, based on your organization's needs. The bigger the company, the more complex it can be, so you really need to think that through prior to implementing.

The biggest lesson I have learned from using erwin DM, irrespective of whether it's for Snowflake or not, is that having the model upfront and getting it approved helps in reducing project go-live time. Everybody is on the same page: all the developers, how they interact, how they need to connect the various objects to generate their ETL processes. It also definitely helps business analysts and end-users to understand how to write their Tableau reports. If they want to know where the objects are, how they connect to each other, and whether they are a one-to-one or one-to-many relationship, etc., they can get it out of this solution. It's a very central piece of the development and the delivery process. We use Talend as our ETL and BI vendor for workload. We don't combine it with erwin DM. Right now, each is used for its own specific need and purpose. erwin DM is mostly for our data modeling purposes, and Talend is for integration purposes. Overall, erwin DM's support for Snowflake is very good. It's very stable and user-friendly and our data modelers live, day in and day out, on it. No complaints. There is nothing that impacts their performance.

I recommend using erwin Data Modeler. You should have a good business case to convince the finance team, as the price is high for Latin America. I would rate this solution as nine out of 10.

There might be some effort to do some cloud work at my previous place of employment, but I wasn't on those projects. I don't think they've settled on how they're going to depict the data. Some of the stuff in erwin Evolve, and the way in which it meshes with erwin Data Modeler, was very cool. Sometimes, your model would get corrupted, but you could reverse engineer it and go back in, then regenerate the model by using the XML that was underlying the model. This would repair it. When I showed this to my boss, he was very impressed. He said, "Oh man, this is where we used to always have to call Sandhill." I replied, "You don't have to do that. You need to do this." That worked out pretty well. Biggest lesson learnt: The value of understanding your data in a graphical way has been very rich in communicating to developers and testers when they recognize the relationships and the business rules. It made their lives so much easier in the capturing of the metadata and business English definitions, then generating them. Everybody on the team could understand what this data element or group of data elements represented. This is the biggest feature that I've used in my development and career. I would rate this solution as an eight out of 10.

The ability to compare and synchronize data sources with data models in terms of accuracy and speed for keeping them in sync is pretty powerful. However, I have never actually used the models as something that associates source. It is something I would be interested in trying to learn how to use and get involved with that type of feature. It would be nice to be able to have everything tied in from start to finish. I am now working with cloud and Snowflake. Therefore, I definitely see some very good use cases and benefits for modeling the cloud with erwin. For example, there is so much more erwin can offer for doing something automated with SqlDBM. I would rate this solution as an eight out of 10.

The biggest lesson I have learned from erwin is the old cliche, that a picture is worth a thousand words. It is truly erwin in itself. When a person asks for a set of tables and they actually see that diagram visually, it really assists in any meeting that you will have. It is key to any meeting you have. I would rate Data Modeler an eight out of ten. The reason for this rating is because I did a couple of dumb attributes and it took me forever to find how to truly delete it. It was a parent-child relationship and I deleted the parent and did not answer the question from the next box that popped up correctly. So I had an attribute hanging out in a table and it took me forever to find the dangling relationships. Because of that, I knocked it down a rating because it did take me a long time to find that. I'm quite happy with the modeling tool. It does just about everything that I need it to do. I can't really think of what it doesn't do that I would need other than the PDF. I'm really happy with it.

erwin DM is good. It does the job and it's been around a long time, so I think it would be a good one to use. I don't have any problems with it. I would rate erwin DM a nine out of ten. Nothing is perfect. I don't have any real issues with it. It does everything we need it to do. It's really good.

The one thing that having a CASE tool does is it takes the drudge away from modeling. You get to actually think of what you're doing. You think about the solution and not how you are going to keep track of what you're doing. It frees you from a lot of mechanical things that are part of keeping track of data modeling, and it allows you to do the thinking part. There's not a lot of documentation on the API. You're pretty much going to have to teach yourself. If you have a specific problem where you've gotten to a certain point, you can always touch base with the guys at erwin and they will help you to get little snippets of code. But if you're doing things like we have, which is to write a full-blown application to extract the data or to make changes to the model, you're pretty much going to have to learn it on your own. That's just the one drawback of the API but if you're a programmer and you want to DM like me, it's a lot of fun. It's a challenge but it's very rewarding to be able to automate stuff that people are doing manually and to be able to hand them a solution. From one out of ten, I'd give erwin a 9.99. Everything has flaws. Everybody's got these little quirks like I mentioned about the ability to make changes that you shouldn't make. But as far as the product itself, I love it. It's right up there with a 10.

The biggest lesson that I've learned is actually with a lack of data modeling. We have teams who have complained that data modeling takes too long. They would rather have developers manually code the DDL, which creates a lot of mistakes, increases the backlog, and increases not only the time to delivery but the cost to delivery. There is a lack of understanding of the agile methodology around data modeling and the incorporation of the emergent design happening in the scrum teams with the intentional design of the data architect creating a data model. Given an opportunity to follow the correct path and perform data modeling, we have seen a significant return on investment with decreases in delivery time and decreases in project cost. I would rate erwin Data Modeler a ten out of ten.

Sometimes you have an initial idea for a data model and when you try to design it in Erwin you realize that you were wrong in how you approached it. Erwin enforces consistency and accuracy. Quite often I learn something by looking at the generated code. It's not like I create table statements all day long. I don't do that generally. So when I use the tool, it generates the correct code in scripts for me which we will then hand off to the DBAs who run them. I would rate it a six out of ten. It's frustrating. It could be so much better. The problem is mostly usability. It has little quirks about the way the screen refreshes, things move around, and the workflow when you're creating columns and tables could be so much better. I have a love-hate relationship. I've used this product for years. I've actually gone to training on it at Erwin, so I know what I'm doing with it. I wish they would make it easier to use. I would think if Microsoft bought it, this would be a totally different product. Interestingly enough, Microsoft has tried to come out with data modeling tools a few times, and they are all bad. They're basically toys. You can't use them for anything real, which is surprising to me. You would have thought that they would have had a tool that could compete. There are only a couple of big players out there that Erwin competes with. I looked at just about all of them, and I keep coming back to Erwin, but I hate it nonetheless. There's nothing better. There are certain tools that are better in certain areas but far worse in others, and so you pick your poison.

It is the only meaningful way to do any data modeling. It is impossible to conceptualize and document complex data environments and the integration between different data subject areas. You can write all the code or DDL you want, but it's absolutely impossible to maintain any sort of conceptual or logical integrity across a large complex enterprise environment without using a tool like erwin. You want to look at what you are trying to accomplish with erwin before implementing it. * Does the product have the ability to support or accomplish that? * Based on the technologies that you have decided you want to use to manage your data, how intimately does it integrate with those technologies? From my perspective of using the traditional relational databases, I think erwin probably works pretty well. For the newer database technologies, such as the Hadoop environment databases, it's not clear to me how successful erwin is. However, I'm not talking from the perspective of somebody who has been aggressively using the latest version. I don't have access to it, so I'm afraid my concerns or issues may not be valid at this point. I will find out when we finally implement the latest erwin version. I would give the solution a seven or eight (out of 10).

I would certainly recommend this product to anyone else interested in trying it out. The support from the vendor is great. The tool overall performs well and is a good product to use. Having a collaborative environment such as the one that erwin provides through the Mart is extremely beneficial. Even if multiple people aren't working on a single model, it's nice to have a centralized place to have all the models. It gives us visibility and a central place to keep everything in one place. Also, it supports versioning, which allows us to revisit it at different points in time to go back to in the model, which is really helpful. We do not use erwin to make changes directly to the database. We have no current plans to increase our usage of erwin other than adding more models. We would rate the solution overall as an eight (out of 10).

The biggest lesson that I've learned in using this solution is to have a data governance process in place that allows you to use erwin more easily, as opposed to it being optional. There are times when people like to do design without erwin, but that design is not architected. It pays to have some sort of model governance or data governance process in place, so models can be inspected and approved and deployed on database platforms. We use it primarily for first drafts of database scripts, both in a relational database environment and other types of environments. The models represent those physical implementations. The database scripting part is heavily modified after the first draft to include additional features of those database platforms. So we find erwin DM less valuable through that and we find it more valuable creating initial drafts and reverse-engineering databases. It cuts development time for us to some degree, maybe 10 percent, but all in all, there are still a lot of extensions to the scripting language that are not included with the erwin product. In our company, there are about 130 users, globally. From time to time the number varies. Most of those users are either the data modelers or data architects. There are fewer enterprise data architects. The other users would just be erwin Web Portal users who want to have a little bit of an understanding about what's in a data model and be able to search for things in the data model. For deployment and maintenance of this solution we have about two infrastructure people, in an 8 x 5 support model.

My advice would depend on how you're going to be using it. I would definitely advise that, at a minimum, you maintain logical and physical views of the data. That's one of the strengths of the tool. Also, while this might sound like a minor thing, it's important to create standard templates — Erwin is good at that — and you can customize them. You can create a standard template so that your models have the same look and feel. And then, anyone using the tool is using the same font and the same general layout. erwin's very good at helping enforce that. You should do that early on so that you don't have to redo anything later to make things look more cohesive. Another feature of erwin is that it can help you enforce your naming standards. It has little modules that you can set up and, as you're building the data model, it's ensuring that they conform to the naming standards that you've developed. I think that's something that some people don't realize is there and don't take advantage of. The biggest lesson I have learned from using this solution faces in two directions. One is the ability to engage the business to participate in the modeling. The second is that the forward-engineering and automation of the technical solution make it more seamless all the way through. We can meet with the business, we can model, and then we can generate a solution in a database, or a service, and this tool is our primary way for interacting with those roles, and producing the actual output. It's made things more seamless.

For our use cases and requirements, we are very happy with the erwin product. If we come across any issues or have any doubts about the tool, we get really good support from erwin support team. They definitely have a positive impact on overall solutioning because of how they design and capture data. This is definitely something any company who is involved with data should look into, specifically when there are many database platforms and dealing with huge volume of data. It is definitely scalable as well, as we are one of the biggest financial institutions and have a very massive Data Models inside this tool. The biggest lesson learnt from using this solution is how we can capture metadata along with the data structure of the database models. Sometimes, when we go to the business showing the designs of the conceptual/logical model, they want to understand what the table and each field is about. So, we do have an option to go into each entities/attributes to add the respective information and show them the metadata captured for these entities and attributes. I would rate the newest releas as 9.5 out of 10. When our requirement use case change, the solution moves to a newer version and everything works fine. We are happy with that. However, as time goes, a year or two, we might come across some situations where we look for better enhancements of features or newer features.

Take the time, especially if you're going to use Workgroup, but even if you're using desktops, to figure out how you're going to manage the models. They need to have a naming convention. They need to have a directory organization that makes sense to you. They need to have change-control, just like code. You need to figure out how you're going to use it because once it gets past 50 models, finding something and knowing how to change it and where to change it and where to publish it back out is going to be your biggest headache. You need to think long-term. It's easy when you just have a few models. As soon as you have 1,000 of them, unless you've thought ahead, you're going to have a huge cleanup problem. The biggest lesson I take away from using erwin Data Modeler is that we should all be doing much better library sciences with our data assets than we do. erwin is a great tool to capture your library sciences. It can tell you what you need to know about a piece of data, or a row of data as a dataset in a table, or a collection of tables. You can add information not just about single things but collections of things. We should have many more people whose job it is to add that value. Right now, companies still mostly use erwin for custom development and it needs to be much more built into documentation of any type of data. I use erwin to do data models of reports and of API calls, for example. Any data set, to me, qualifies as needing a model so that you can tell what data elements are in it and what that dataset is used for. Through all the years, erwin has done a great job of making things better and better. There are always things that we're talking about in terms of improving it, but the fact that it's now starting to integrate better with data governance-type tools so that all of your definitions can move to more of a glossary form, rather than just being in the models, is tremendous. The more that that's integrated back and forth, the better it's going to be. Out of all of the modeling tools, erwin is a 10 out of 10. It hits all the high points for me. There are some pieces of functionality that competitors come up with, maybe a little bit earlier, but it's a leapfrog-type of thing. Every time the vendors find that something is needed in the world of modelers, they all start to bring it in. I find erwin to be very responsive to those needs. So now, erwin has NoSQL modeling aspects in the tool and they're connecting with their own suite of data governance tools. That means you can push definitions to your data governance tool or bring them back from your data governance tool. It's starting to become much more of an integrated solution, rather than just a standalone.

If you want good data architecture in your company, you need to have database design done. It's probably the most important factor for having things clearly modeled and documented. erwin Data Modeler is not just a modeling tool, it's also used for documentation. If you're using the tool's functions properly, analyzing the documentation, flagging fields that are NPPI data, it is invaluable for business use. You can generate data dictionaries, you can make sure people are speaking common languages, and you can enforce company standards so that people are doing things in a consistent manner. It's an invaluable tool. If you want to have good data architecture, you need to have a tool like this. We don't currently use the collaborative web modeling capability. We just recently purchased that tool and we are planning on deploying it at the end of Q1 of this year. We don't use the erwin data transformation for integration to a wider ecosystem. We are actually able to directly do all of the transformations that we need from erwin, so we're not required to do any transformations. It supports legacy systems like Db2, Oracle, SQL Server, and now Teradata and Hive, which were introduced in the past few years. But it can currently support all of the data modeling we need to support, so no transformations are needed. We have different flavors of people who use the tool. We have people who are dedicated data architects, that's their full-time job. There are 15 to 20 of them in the company. And we have many people who do use it for very specific applications on more of a part-time basis, where they're doing the data modeling and reviewing it with an enterprise architect. There are about 150 people who are doing that. Overall, we have about 170 people who have access to the software. For deployment, upgrades, and maintenance of the solution, we generally require four people. We require somebody to do a Windows upgrade; we require somebody to do a database upgrade, and that's for the Mart repository portion; and we have two people who do the testing for the erwin tool: somebody who installs the upgrades of erwin on the local machines, and somebody who's testing it. When it comes to the installs and the upgrades, each person who's using the tool is expected to do that on their own. We set up a deployment package and everyone runs it when they're told to execute the upgrade.