erwin Data Modeler Reviews

I primarly use erwin Data Modeler for modeling purposes. Once we have the business requirement, we create the models in erwin Data Modeler, and then we perform forward engineering to get the query set and create the tables accordingly in SQL Server.

The best features of erwin Data Modeler include the forward engineering tool, which gives you the schema according to the data model that you generate. When working in legacy models such as data migration projects, the reverse engineering tool is beneficial because you can directly get the older models from the legacy system, including old data warehouses, then obtain the data models accordingly, make a few changes according to the business requirement, and create the new model.

The UI in erwin Data Modeler can be improved significantly because when any new person opens the tool, all the toolbars are kept open with many tools in view, making it appear chunky. If possible, suggestion models could be added. With AI tools available, whenever someone creates a data model, the tool could suggest better ideas to normalize the data models further based on knowledge and normalization principles.

I have been using erwin Data Modeler since 2021, though not continuously; I have used it on a few projects.

I rate the stability of erwin Data Modeler highly; it is a stable product.

I rate scalability at nine.

Overall, considering other feature scopes, I rate erwin Data Modeler at eight. My overall review rating for this product is nine.

For erwin Data Modeler, the use case was to create a data model for the organization. It articulates how data flows throughout the organization. For example, we have an employee table with two types of employees: permanent employees and contract-based employees. Contract-based employees are hired from an external organization. In the table structure, we would have multiple tables including employee, permanent, contract employee, and external organization. However, we did not want multiple tables. We wanted to combine them into one super table to reduce the normalized form. This approach is called a super-type rollup.

We did not use erwin Data Modeler for AI purposes. It was used for the migration part from SQL Server to the Azure Cloud. I did not work on the AI part for that project.

The project I was working on is based in the finance domain. For whatever quarter expenses our client had, we needed to communicate that with the client-side finance team management. That communication was not handled by me. I created the model and handed it over.

The best feature that I loved about erwin Data Modeler is the transformation part. We can create two types of models: the physical and logical models. Suppose there are multiple relationships such as one-to-one relationships, many-to-one relationships, and many-to-many relationships. If we want to reduce the size and optimize the data model, there is a transformation with an inbuilt functionality that helps resolve these issues. When one table is reflecting to ten other tables, it becomes very difficult to find relationships between them. If there are tens and hundreds of tables, finding relationships becomes increasingly complex. However, erwin Data Modeler has an inbuilt transformation that helps resolve many-to-many relationships and provides a better data model. This means I do not have to manually go through each and every table to find the relationships involved.

The tool is also very intuitive and easy to use. It is a simple drag-and-drop interface where we can create ER diagrams and import multiple projects. There is an Excel file where we write all the schemas and columns present in the table along with their data types. This Excel file has a VBA script. Once we finish putting all the data into that Excel file, we generate the model using that VBA. We then import that file into erwin Data Modeler and it is almost done. The transformations and other operations are the later part of the process.

As we all know, AI is taking up a majority of things. I felt that erwin Data Modeler is a bit of an outdated tool. It needs a new look and integrating AI into it would be much better for learning. AI would guide us on what operations we have to go through. If we are stuck somewhere, we could query the tool directly and ask about data modeling operations, and it would help us. Otherwise, we have to search on YouTube or Google or other sources to learn how to use erwin Data Modeler. An inbuilt guide within the tool would be much better, and an AI guiding tool would be even more helpful.

I have been working in my current field for three and a half years.

I have never faced any instability issues.

I did not have the opportunity to try other data modeling tools because my company had only one data modeling tool with just one erwin Data Modeler license.

I joined the company after erwin Data Modeler was already deployed. Whatever new data models came up, I had to make the data models and hand them over to my superiors.

I have no idea about the setup cost because I did not buy the license.

I would appreciate more information because I do not have much knowledge about PeerSpot. The team that contacted me on LinkedIn talked with me about erwin Data Modeler. I worked on it for four to five months. The last time I worked on it was last year in August. It is not difficult to learn how to use erwin Data Modeler. It is very easy, and there are reference materials available on YouTube. I did not face any difficulties learning the tool. The issue was not with erwin Data Modeler itself but with the organizational constraints. I did not have access to everything, which prevented me from exploring the tool further. I would give this review a rating of eight out of ten.

I am using the solution for data modeling. We also use erwin Data Intelligence Suite for data governance.

We like that we can go straight from Data Modeler to Data Intelligence Suite. We can go from the business glossary to the data catalog. It gives us a holistic view. The product lets us import different types of models from various databases. It also allows us to export or implement them within the same databases based on new changes.

The modeling capability is cumbersome. It doesn't allow us to put different types of objects in there. We can only put data objects. It doesn’t allow us to put any class structures and enterprise architecture objects. We can only do data monitoring. We can't do a holistic view. It's quite cumbersome to enter data. We cannot type things into a sheet. We always have to open the object and enter it.

The layout is a very old-style diagram. The product must allow users to put different types of objects and link them to data objects. The tool must provide large data objects under which there can be other data objects. For example, if a customer is a large data object, it must then contain the customer’s name, address, and type. The interface must be improved.

I have been using the solution for more than ten years. I am using the latest version of the solution.

I rate the tool’s stability a seven out of ten. It doesn’t stay up all the time. The stability must be improved.

I rate the tool’s scalability a seven out of ten. We have some issues with performance, but I'm not sure if it's us or the tool. We have 50 to 150 users.

The technical support team is quite helpful. The team members don't always know their stuff. Sometimes, we end up teaching them things, but they do try to be helpful.

Neutral

I have used Rational Rose, Visual Paradigm, and Oracle’s old product. I use Visual Paradigm for my personal use cases. I prefer Visual Paradigm. It has a lot of features that erwin doesn’t. The only problem with Visual Paradigm is that it has many license levels. I only have a cheap license. However, its capabilities are much stronger. It is a lot easier to use than erwin, and it produces much nicer diagrams.

The solution is cloud-based. We use both AWS and Azure as our cloud providers. Maintenance is done automatically by the vendor.

The tool is expensive. It is not worth the price. I rate the pricing a three out of ten.

We are looking for other tools to replace the solution. We're going to do an RFP for data modeling, data catalog, data quality, and marketplace. The vendor is trying to charge for Data Intelligence Suite like it is an enterprise tool, but it's still getting patched together. I have a hard time giving erwin a strong recommendation, but it's a tool that works and does what it says on the box. It could be made a little better. Overall, I rate the product a seven out of ten.

I use erwin Data Modeler for a metadata project. I don't have thousands of tables to manage or a data warehouse or anything.

erwin Data Modeler helps us reverse engineer and keep track of data before converting from one tool to another. erwin allows us to track data and share it with business analysts.

We can create mappings in erwin and possibly data dictionaries. You can also check in to GitHub or Azure DevOps.

erwin is not as robust as a data warehousing project I've been on in the past. Likewise, the solution is a little pricey.

I recently started using the current version, and I've used the prior version for about four or five years.

When I was on versions 7.1 or 9.0 and had a very large database with hundreds of tables, sometimes the tool would fail. I don't know if that happens on this version or not.

I rate erwin DM's scalability a seven out of ten.

Deployment isn't a problem for me because I've been using the tool for years, but a new person will just have to get their feet wet. We initially had problems installing it because of handshakes between Black Knight and Quest. Black Knight doesn't allow certain things to come through, and we should've known better, so it took a long time to get it installed and figure out what the issue was. It took four to eight hours to get resolved.

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.

We use erwin Data Model to create logical and physical data models.

The solution has been very helpful. The logical and physical data models provide visualization for communication across the business, among the data team, and technical team. Without this solution, communication would be difficult.

The ease and quick ability to create the model is valuable. The fitting model is very intuitive. 

The other cool feature is the reverse engineer feature. If we connect to the database, we can reverse engineer from the database itself of the physical model. That gives us the relationship of the data if not much documentation is available.

So far, we have used it in either a workroom version or a standalone version. It does not have a cloud application.

We have licenses for Mac Linux since a lot of our people have Mac laptops, so erwin should work on their cloud application.

I have been using erwin Data Modeler since 2006. 

It is a stable solution. So far, we have not had many problems. Sometimes it freezes because it is a resource-hungry application, so it uses a lot of memory and a lot of processing power while running. But so far, we have not lost any of our work.

We had a concurrent license where multiple people could it. Typically, this is not a tool that you’ll use for hundreds of people. It’s very scalable for ta limited number of people.

The technical support team is pretty prompt. The tier-one support is pretty prompt. We contacted them a couple of times for a security patch and a few times for transferring the license to a different user. Their support was good.

Positive

I have tried Lucidchart, PCO, and ER Studio.

erwin and ER Studio are very comparable. I personally think both are more or less the same. However, erwin has richer features, and productivity is boosted. Once I have a model in mind, putting that in the tool is very quick, followed by generating the DDL, creating the table, and integrating it with the data explorer. The bulk editor feature is also very useful. 

The initial setup is straightforward for someone who understands the data.

For the workgroup version, you need to set up a database and then connect to the database. After installation, you have to maintain those connections. For someone who is well-converged with a database, it’s simple. If someone has not worked with web databases, they might find it a little complex. The deployment was done within a couple of hours by three to four people.

The deployment was done in-house.

There is either a one-year or three-year license. It is not a pay-as-you-go license like a SaaS solution. It’s more of a traditional licensing. They are a little bit on the pricier side.

However, there are tools that are less pricey, and some of them are open source and free as well.

From a pricing perspective, it is not very competitive in the current market.

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.

We are implementing erwin to streamline data modeling and automate network changes for increased efficiency and comprehensive data development. This option simplifies the need for better data indexing.

Quest can create conceptual and physical data models to facilitate effective communication between business and IT. It can also integrate expertise from various engineering and database systems within your organization to enhance data management and administration.

I have been using erwin Data Modeler by Quest for four years.

I rate the solution’s stability an eight out of ten.

We are testing the capacity in our environment for more than six thousand databases.

When we were working with third-party providers, they were giving very good support.

The initial setup depends on the tool if you are using the data model environment. Stopping the server could be complex and require strong expertise.

If you have everything set up and configured, Erwin is straightforward. You can deploy it in a few hours. In our case, it took a couple of months to address internal conditions, assign machines, and set up everything for the services.

I rate the initial setup a six out of ten, where one is difficult and ten is easy.

The reseller set up the whole department.

The product is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.

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.

We use the solution for data modeling.

We believe that the solution would help provide some structure to our data modeling and data design practices.

The product allows us to reuse entities and attributes. Whether we're working on a conceptual or logical model, once we have defined them, we can reuse them rather than defining them all over again. It's a clever feature and helps with our work.

We can only get licenses through partners. We cannot purchase directly from Quest. The partners end up charging a big margin on top of the actual price even though they are not providing any service. It was a letdown.

I have been using the solution for four months.

I rate the tool’s stability an eight out of ten. The tool has crashed a couple of times. The stability must be improved.

I am the only person using the tool in my organization. We plan to increase the usage, but it is currently not being used much.

The support personnel are a bit slow in responding. The vendor talks about providing 24/7 support, but it could take 24 to 36 hours to get a response. The communication is just via email. It slows down the whole troubleshooting process.

Neutral

The initial setup was easy. I rate the ease of setup a ten out of ten. It took me five minutes to deploy the product. To deploy it, I downloaded the software, ran the executable, and it installed itself. I did the deployment myself.

The solution is expensive. I rate the pricing a nine out of ten. The price is not fixed. The product does not have a clear pricing model.

We evaluated ER/Studio but did not like its pricing structure. They have different versions which require multiple licenses for the applications we use. It was not easy to use. We preferred the pricing model of erwin Data Modeler.

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.

I use the solution for reverse engineering as well as forward engineering. I do logical and physical modeling, and some of what I'm doing right now is reverse engineering from actual databases because they have no diagrams. The solution helps me diagram the current and help me design the future.

Forward engineering, DDL generation, reverse engineering, and reporting are the most valuable features of the solution.

The solution's model mark could be better because it crashes sometimes.

I have been using erwin Data Modeler by Quest for many years.

erwin Data Modeler by Quest is a stable solution.

I rate erwin Data Modeler by Quest a nine out of ten for stability.

To add users, you have to start using the model mark, which is not great. I rate erwin Data Modeler by Quest a seven out of ten for scalability.

The solution’s technical support is good.

Positive

I have previously used IDERA ER.

The solution’s initial setup is easy.

I have seen a return on investment with the solution because I can't do my work for my company without it.

erwin Data Modeler by Quest is an expensive solution.

Before choosing erwin Data Modeler by Quest, I evaluated PowerDesigner. I chose erwin Data Modeler by Quest because it has the most features I might need in the future, and the report writing is really good.

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.

I use the solution for entity relationship data modeling to build the databases.

Drag-and-drop data modeling and reverse engineering out of databases are the most valuable features of erwin Data Modeler by Quest.

I would like the solution to be less rigid in terms of its theory. It supports the theory very well, but it's too rigid. It's focused on database design and theoretic database design. I'd like it to be able to do better extractions.

I have worked with erwin Data Modeler by Quest for 20 years.

The solution is too rigid in terms of usability. The other thing is you can do things to make it crash, and when it crashes, the models created with it become read-only. To be honest, I prefer using Visual Paradigm.

I rate erwin Data Modeler by Quest a seven out of ten for stability.

It took some coordination to get the license transferred to me. Otherwise, the solution's initial setup is straightforward.

I've probably spent half a day not running the installation but waiting for emails back from the vendor.

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.

We are using it for a very specific use case, and it works pretty well for us. We do all of our database modeling based on this tool, and it is a repository of all data models in our business intelligence ecosystem. The logical representation of our metadata and anything that is created in a database, such as tables, is in it. 

It is an on-prem workgroup. We have a workgroup server that hosts our model.

We utilize it for its cross-database capability and logical representation of the data model. We have recently started to use its collaboration features, and we also use it to define all our relationship constraints and referential integrity within our data model. So, a lot goes out of it.

It has standardized our practices. For example, all customer-related entities and attributes have to follow a certain naming convention. It has helped in standardizing the process of creating our data models so that when we go and explore the data, we can combine them in a way in which we are confident of producing the right results. It has made a lot of difference in terms of naming standards, processes around our metadata, and the schema in which we create a database. We have a proper template to put the information through a well-structured data model. It helps users in getting the maximum value of the information that is available in the BI ecosystem. erwin Data Modeler makes it very simple and easy to navigate our very complex data.

Its visual data models are very good and helpful for overcoming data source complexity and enabling understanding and collaboration around maintenance and usage. We have a complex business environment where we have retail and supply chain space for distribution. There are a lot of cases where we use the models for customer promotions and events and loyalty systems. Different data modelers can do their own subject areas, and then they can bring them together in a workgroup workspace. It has allowed us to collaborate and distribute the data modeling work. Previously, it used to be very single-threaded. Now, a lot of different teams can run their own modelers, and then, later on, integrate them, which is very useful. It is also very useful in the database migration process. You can take a logical model and seamlessly transfer it over to the database. That's very useful as well. 

We use its modeling support for Snowflake Cloud. We don't use it in any special way. We use it the way we use an existing on-prem database. It just needs to follow Snowflake conventions, which it does. We have a standard logical model that can then translate to a physical model for any database we choose, and that's where erwin has been very helpful. We can set those naming standards, and it also does logical to physical translation seamlessly. This support for Snowflake is helpful. We have enough help to port our model from DB2 to Snowflake in terms of model creation. It has proven very helpful that way.

It can create table structures across a wide variety of sources, which is very useful for us. It cuts the development time of our database code quite a bit. Otherwise, we would have to rely on Excel sheets. Currently, our average project size is anywhere from 3,000 to 4,000 hours, and out of that, we spend around 5% on data modeling. If we didn't have this tool, it will take almost twice more time for any project.

It allows us to create logical data models. We can represent a database model in business terms, which is very useful for us. 

It supports a wide variety of databases, including the latest ones. We have chosen to go for a cloud-based database, and it supports that, which is very useful. 

It is very useful for maintaining relationships between tables. We can put constraints and foreign key-primary key relationships into the model, and it gets translated into the physical database seamlessly. 

Workgroup is another useful feature to store and share the models with the team for collaboration. 

In terms of improvements, support could have been better in terms of installation, especially of workgroups. We struggled quite a bit to get it up and running. Collaboration could have been better from an installation perspective, but it is trivial as compared to what we use it for. Other than that, I don't have much feedback. It works pretty well, and the fact that we've been using it for more than a decade shows that it is quite solid. 

In terms of new features, it would be great to have a cloud base. We should be able to put it on the cloud for better collaboration and data models sharing.

I have been using this solution for more than a decade.

It is very stable.

It is fairly scalable. We really haven't pushed it to the limit with respect to scalability, but we haven't found any issues.

Currently, we have around 20 users. They are mostly data modelers and data engineers. We have plans to increase its usage as deploy additional systems in our business unit. So, there are plans to scale up, but not in the immediate future.

I have interacted with their technical support. I would rate them an eight out of 10.

I have been in this company only for two years, but from the licensing, I know it has been more than 10 years. I am not aware of any other tool being used previously.

There wasn't a lot of stuff. When things didn't work, we had to go and figure out why this isn't working and which ports should we open. There was a lot of back and forth communication with their support, and they were very helpful, but it gets pretty difficult when something that could be done in one to two hours takes you longer than that. It took us a few weeks to get it right, but once it started working, it was pretty seamless.

There was no implementation strategy. You just download an installable and install it. The problem is that it requires a database, and it requires a particular configuration. All this is documented, but it doesn't work the way it is documented. So, it took time for us to figure out, "Hey, this thing is not working. Why is it not coming up?"

For maintenance, we don't have anyone. For the deployment of the workgroup, it took just one person. My data engineering lead just went and did it all by himself. It is a pretty simple product. It just took us a while to figure it out, especially the collaborative tool. Generally, it is supposed to take half an hour for one person.

We installed it ourselves. We did not use anybody to install it, maybe that's why it took us longer.

I don't have the metrics, but I would say we have seen an ROI. It has brought down the cost of implementation in terms of manpower. It might have saved us thousands of hours. It could also be more than a hundred thousand hours. 

The accuracy and speed of the solution in transforming complex designs into well-aligned data sources make the cost of the tool totally worth it.

There are no costs in addition to the standard licensing fees.

In general, for its purpose or use cases, it is the best tool in the market. It does its part in terms of metadata, but we have other challenges that erwin cannot resolve. We have a large pool of legacy data sources that are not labeled, and erwin really can't help there. I don't see any other tool filling that space unless we go for a catalog, which is a different product space altogether. erwin can process the legacy files, but we're just not using it for that because we don't have the bandwidth.

You need a skilled modeler to start off. It really depends on what kind of organization is implementing it: small scale, mid scale, or big scale, but collaboration really works. It is a very good tool, but proper training would be required to take full advantage of the tool. It helps to do a lot more on the job. You would need a lot of discipline before you start using the product. The standards and governance should be put up front before it can be utilized effectively.

The biggest lesson that I have learned from using this solution is that it cannot resolve governance issues. You need to have proper standards in place before you start using this tool. Bad processes lead to bad outcomes. The tool will help you shepherd those processes, but it doesn't solve them. So, you need to have proper process governance and standards. You need to make the tool enforce those processes and standards. You should have proper controls on the data inside in order to get the best results. Governance and process discipline are pretty important. 

On the database side, I come from organizations where some people follow one standard, and other people follow another set of standards, and if we use the same database and tools, then you get a mess. That's where the process discipline comes in for unified governance, which has got nothing to do with the tool. It has everything to do with how the organization is structured. The tool will help you to control that.

I would rate erwin Data Modeler a nine out of 10. If it can be on the cloud without any installs, that would make it a 10.