erwin Data Modeler Reviews

The easiest way of exposing or sharing the model to all the users at the enterprise level is the feature I found to be valuable in the solution.

They should merge all those products because apart from the Mark Server, they have the Web Portal, which requires a separate installation and license. And it's not easy to maintain, but very easy to use. So you need additional resources such as memory, unlike if they merge those Web Portal and Mark Server, which would be a great solution.

I think they should be more open to or ready to be agnostic to any databases, such as MongoDB. Any database available in the market should be ready in their drivers because I don't think they can be ready. So all the popular databases like Oracle, MySQL, Microsoft, and SQL Server, but I haven't seen any connector in Cosmos DB SQL, so it should be in the big data or cloud solution.

I have been using erwin Data Modeler for a year.

It is not a very stable solution. I rate the stability five out of ten.

It is a scalable solution. Around 20 users are using the solution presently. I rate the scalability an eight out of ten.

The technical support team is fine.

Previously, we used SAP PowerDesigner. One of the primary reasons we switched to Data Modeler is that we need to expose our models so that the user can access them very efficiently and time-bound. If I develop a model and if it's a room, I can easily push that model into a web portal. The users can study and use it from there, unlike in the previous SAP PowerDesigner. You need to export an image or picture file or JPEG or PNG; then you have to share on SharePoint and upload it in Confluence of this. That is a very tedious way of sharing the model. The stability of the solution needs improvement.

The initial setup is straightforward. The solution is deployed through the desktop version available for Data Artifacts. There is also a repository, the data node, and a web portal which is the application or web application for data analysis and other users interested in starting the model. I did all the deployment.

The pricing of the solution is cheap. I rate the pricing a five 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.

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.

We use the Workgroup Edition for sharing data models across the organization. The primary reason we're using the Workgroup Edition over the Standard Edition is the centralized repository of models.

Standard Edition requires individuals to determine how to share data models, whether you share them in a local LAN directory, through email, SharePoint, or Livelink. You have to come up with your own versioning scheme and your method for sharing models. With the Workgroup Edition, because it has the centralized repository with version control embedded in it, it standardizes how your organization does versioning and centralizes all the models at the same time.

The solution's ability to compare and synchronize data sources with data models is fantastic. We use it for that on a regular basis to make sure that changes haven't been made to the database outside of the modeling process. I can take existing databases and reverse engineer them and understand their structure within 15 minutes. If I didn't have Data Modeler it would take hours. It increases our productivity and helps in understanding our legacy application.

The organization that I'm currently with has not historically done data modeling as a discipline. We're using the tool to introduce data modeling into the organization because it is an intuitive tool. There are other modeling products on the market that aren't as intuitive, tools that are currently in use at my company. With this tool, we have an easier ramp-up with our new staff because it is more intuitive.

The solution's support for Snowflake affects our organization's data modeling tremendously because we didn't have a decent tool for the Snowflake product. There are a couple of tools on the market, but they are not good modeling tools. The fact that erwin Data Modeler supports Snowflake is a huge benefit to this organization. The existing procedures couldn't be satisfied with the tooling that my company was using. It's a huge plus because it was difficult for teams that are migrating to Snowflake to adhere to the internal company standards of data modeling, because there were no products that offered Snowflake as one of the solutions.

It also reduces development time because you don't have to write DDL manually anymore. You use the data modeling tool to generate the DDL. It reduces development time by about 10 percent. Workgroup's code generation helps to ensure accurate engineering of data sources, which reduces the number of errors during development. If someone is writing DDL by hand and they don't get the syntax right, they have to figure out what's wrong with it to be able to fix it. It helps you build your table structures more quickly so that you can actually begin development of the business logic in your application layer.

The most valuable features of the Workgroup Edition are the

• centralization of the models
• flexibility of the directory structure
• application of the naming standards across all models in the repository.

The centralization of models helps share models across the organization. Instead of having to email someone and say, "Hey, where did you put the model for this?" it's easily found. It also makes it easy to organize models. When you work in a large organization that has more than 1,000 models, you need to be able to organize them in some fashion.

Historically, the Workgroup Edition had a flat structure so you had to name a model in a particular way to be able to find it, when you had thousands of models. With the current version, because you have a flexible directory, you can organize your models any way that your organization feels would work well.

In addition, the visualization side of erwin really helps people to understand the structure of their data. It greatly enhances their ability to create the appropriate modifications to their existing structure, because they can graphically see how their structure is currently laid out. It helps with maintenance on existing applications, and it dramatically helps, when you're doing greenfield, in understanding your data requirements in a graphical format. The graphical aspect helps non-technical people to understand the database design. For the non-technical folk, it is very helpful for understanding the design and whether or not the design is meeting their requirements.

For anyone who's interested in the data design of an application, or a warehouse, the erwin Data Modeling tool is very helpful. That's especially true for people who don't understand the structure of databases. It helps them understand the relationships between tables, and what is contained within a table. It's an understanding that they don't have without this kind of product.

I would like to see the ability to support more NoSQL platforms more quickly. 

In addition, enhancing the graphics to render more quickly would be beneficial for any user.

I've been using erwin Data Modeler since 1992. In my current company, they were already using Data Modeler when I started in December of 2020, but I'm implementing the Workgroup Edition. They didn't have that.

Workgroup is a really stable product. 

The challenges I have found with it, from a stability perspective, are simply because of the sizes of the organizations that I have worked at. The sizes of some of the warehouses we have are quite ridiculous. When you get upwards of 5,000 tables and a million columns in a single model, the tool shows symptoms, primarily, of memory issues, and it becomes slower. To be frank, it's a result of poor design of a legacy database in the first place. It's not a fault with the erwin product, rather it's the fault of the people who designed that database.

In the past, we have had problems with scalability, but the vendor has fixed all of those issues over the last two years. I contacted them directly and they fixed things in the product for all of their clients.

Their tech support is adequate. It's easy to elevate an issue if I run into support personnel who aren't capable of handling it.

The company that I'm with now uses IBM InfoSphere Data Architect. We're switching to erwin because the IBM product is inferior and it is in maintenance mode and will not be supported. It also does not support Snowflake or any non-relational databases, such as Cassandra or Couchbase or Mongo. Workgroup does.

The initial setup of Workgroup is easy. The installation software is very straightforward.

You make the rest of it as difficult as your organization needs it to be. The directory structure that you establish is dependent on what your organization requires. In addition, it offers two different methods of user-authentication and four different profiles to manage the security of who can do what within the centralized repository. It gives you complete flexibility for your data modeling practice to be as open or as closed as your organization desires.

The amount of time the deployment will take will be dependent upon the specifics of an organization's modeling. In a large organization, you can't just install software. You have to go through a process that takes weeks, such as packaging the software for distribution onto desktop. The back end of the Workgroup Edition, which provides you the centralized repository, is an easy implementation. It's the directory structure, and the security that you want as an organization, that drive complexity into the deployment. If I wanted to let every person in the organization modify every model, I could be done in a day. If I need 20 different Active Directory groups and different levels of security, then I have to think through how I want my organization to use the tool, to set up the security appropriately. The tool itself makes it very easy to do that. The hard part is deciding how you want your organization to operate.

We work directly with erwin.

I don't think we'll have any return on investment in the near term. It's more of a long-term investment and a change in the culture of the company. It will take time before it shows any ROI.

The tool is definitely worth its price.

We looked at ER/Studio. We chose erwin because ER/Studio does not support Snowflake or Cassandra or Couchbase. They do support Mongo, but that isn't a database that we use.

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 solution is primarily used for banking data models.

I have worked with erwin Data Modeler for quite some time and familiarity is its most valuable feature. 

The reverse engineering in Oracle Databases needs improvement, as there are issues. 

I have been using erwin Data Modeler for twelve years.

It is a stable solution. I rate the stability an eight out of ten. 

The solution's scalability depends on the device that you use it on. 

The technical support team is good. I rate the support an eight out of ten.

Positive

Previously, I used SAP PowerDesigner.

The initial setup is straightforward. The deployment takes a few minutes when entering the license and installing it. 

It is not a very expensive solution. Only the licensing and maintenance fee needs to be paid. 

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.

Our primary use is for doing database designs on just about any platform. The main users are dedicated data architects, while we also have development team staff using the tools to review models. Additionally, our database admins access the solution for implementing the Data Definition Language (DDL).

The solution is excellent in providing a visual representation of a database and can generate DDL for implementing changes. We use DDL for logical purposes to review with business people, ensuring they have the required fields for processing. We also use it as a data dictionary for the physical data model to understand all the purposes of the terms. This helps us map the logical and physical terms with the business definition to understand our data.

Data Modeler is straightforward to use and fulfills all our requirements. 

Although Quest Software has made tremendous strides in recent years, they need to evolve more in the big data arena; erwin Data Modeler could use a little more work when it comes to big database designs.

We've been using the solution since the 90s. 

The product is very stable. 

The platform is highly scalable; we use it for our entire enterprise data architecture. 

The tech support is pretty good, but there's always room for improvement, so I rate them eight out of ten.

Positive

Our initial setup was over 20 years ago, but I know it to be almost effortless; it's a basic software installation that takes about five minutes.

Regarding maintenance, there are occasionally new versions and releases, and we have dedicated data architect teams that coordinate so every team upgrades simultaneously.

We've continued to use the product for many years and compared it with others on the market. The pricing is reasonable considering what the solution offers and what we pay. There are cheaper tools, but they may not be as robust and easy to use, so it's worth the money.

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.