erwin Data Modeler Reviews

My main use case for erwin Data Modeler involves using AI agents to assist with implementing these models within the Adobe Experience Platform. That is my primary experience. erwin Data Modeler serves as a blueprint that helps guide the implementation of the corresponding schemas in the platform. By defining the structures and relationships up front, I can use the models in the platform itself by defining the relationships. This approach helps me build the gaps between data modeling and implementation, making the overall process more structured and repeatable. This highlights how dedicated modeling tools can complement newer assisted developments.

A specific example of how I have used erwin Data Modeler in a recent project is that the broader perspective of implementing AI was to migrate some of the existing models that were part of the regular EDW environment to move them to Adobe Experience Platform where the process is to convert normalized data into denormalized data, given that the platform is only used for marketing purposes and it is valuable for a direct way to translate the structure schemas, including appropriate XDM structures, field configurators, and the use of cloud platforms to integrate this process seamlessly. That process was something that I worked on using erwin Data Modeler, and it made my job easier because the data model designs were smoothly translated and with the assistance of AI, it made the process much quicker.

erwin Data Modeler is part of the regular process where we translate all the data structures and blueprints into a more structured way of designing the documentation, rather than spending hours just to keep the process more lightweight and help connect these models integrated via the connectors, which will bring the data directly into the platform, or rather the schema, into the platform, making the work process much faster.

erwin Data Modeler sits within multiple sections of my broader data governance stack, primarily by creating data models. It is then used as a feed to validate against the data blueprint that we cross-check against and eventually pipe that into a feed used for validating the structures before any deployment or any new changes are introduced within the ecosystem of the EDW.

Data models and their metadata play a key role in my AI or analytics initiatives, as they feed the data to the model, enabling it to understand what exists and what types of changes are being made on top of it or what amendments we need to make given the new business requirements or changes that come regularly. This is a pivotal point for making decisions based upon the changes we receive.

Regulatory, audit, or privacy requirements significantly shape how I use erwin Data Modeler, as the audit process is governed by the changes we make or new changes introduced because of legal requirements from our clients. They undergo a review process to accommodate any legal changes inputted by the business as well as the legal team.

Currently, my team primarily maintains erwin Data Modeler for our ongoing project. There are other parallel projects that depend on case-specific use, and we have not come across a specific case where erwin Data Modeler does not help us resolve the majority of our workloads.

Overall, erwin Data Modeler has positively impacted my organization as it is a useful option for organizations that need a structured approach to data modeling and data design. In my work, it added value and extended beyond traditional database designs. I use the model as a reference for designing customer data structures and planning denormalization for the processes where we use other platforms like the Adobe Experience Platform and AJO. Guiding the AI assistant has made the process even more seamless as large language models help to easily integrate with erwin Data Modeler.

Since using erwin Data Modeler, our observation is that it has reduced about 10% of our workload in the context of the versioning problem that I explained earlier. It has helped in greater productivity of 7% to maximize the deliverability we have for projects we work upon. Overall, it brought a significant improvement in our processes where we have to make changes constantly into the data designs, which can be integrated seamlessly. That has overall effectively brought our workload down to about 3 to 4%.

It generally takes my team less than 10 minutes to design and deliver a small change using erwin Data Modeler. However, if it is a whole new project from scratch, it would probably take us close to two hours to design, given that the requirements have already been finalized. If the requirements are not finalized, then that process is a little more conditional since we must have those discussions prior to building the model. Overall, creating and adjusting or amending the ERD diagrams is much faster.

erwin Data Modeler does help improve data governance frameworks as the changes we make based on business needs incorporate collaborative platforms for sharing documents or the changes we make within the platform. This sharing allows the legal team, who may not have direct access to the platform, to access links to review changes and assess the impacts of the modifications introduced.

erwin Data Modeler helps ensure that our data is AI-ready and reliable as it aids in preparing our existing data structures against our requirements. It identifies gaps or missing fields and evaluates and recommends changes that were not included in the data model, providing a focused way of identifying what needs to be included before entering the development phase.

Documenting or reverse-engineering a legacy or undocumented system with erwin Data Modeler typically took one to two business days, which has now reduced to close to a business day given that we leverage AI-generated data documentation. It provides a clear indication of what changes were introduced or what effective changes are needed for project enhancements, helping seamlessly reduce the workload for our team.

erwin Data Modeler has reduced work, defects, or failed deployments by identifying roughly 3-4% of errors, such as notifying data type changes given that the metadata was pre-provided. It helps us identify edge cases that could cause error failures in case certain data was productionized. This information is critical for sharing back with our business team to cross-check if these data points will appear in the future, effectively helping us guard-rail our data models.

erwin Data Modeler plays a critical role in strategic decision-making within my organization, as we consistently utilize it for a structured approach to changes we make to database designs. For teams working with complex customer data and analytics, the combination of visual modeling and implementing the physical blueprint of the data has provided consistency and a foundation for building and maintaining data architectures throughout project systems.

erwin Data Modeler has played a critical role during our migration to a cloud platform by improving the process and speeding up the project plan overall by an estimated 5 to 6%. erwin Data Modeler's seamless integrations with other cloud systems created smooth transitions without gaps, and it significantly reduced our development effort while increasing overall productivity.

erwin Data Modeler's best features that I find particularly useful include its capability of AI assistance with a single prompt that could generate the modeling and then you have a quick way of printing out the connections and post the connections that you can build out the relations between the denormalized data. That is one of the features that I have frequently used.

In addition to the features mentioned, another feature I want to add is about the versioning feature. Lately, we have been facing a lot of problems in terms of versioning. As the data model grows, we wanted to maintain the versioning feature, and that is another capability that I liked. I have used that in our recent projects.

erwin Data Modeler's versioning feature helped us significantly because this was one of the challenges that we regularly faced where multiple team members go and make changes, and it was difficult to maintain historical changes. erwin Data Modeler's versioning kept track of all the changes that each individual made, and that is how it helps us find out any changes that were made to the model or the design, effectively identifying where defects were or if there were changes, how the CI/CD integrations were working seamlessly without causing any further deployment issues.

Creating visual representations of data structures using erwin Data Modeler's enterprise-grade visualization feature is straightforward. There are many toolbars that we can utilize. I would look for options on how to enhance collaboration between architects and data engineers by providing a common reference for data models and designs. In my workflow, that is the ecosystem we work on, and it adds value in ensuring that feedback is seamlessly incorporated with built-in governance capabilities and securities to protect the exchanges we make on the data model.

Transitioning between legacy systems and modern platforms using erwin Data Modeler is generally simple, as features have been added to import the DDLs, which most platforms have. There are a few edge cases that require additional features to ensure the process goes seamlessly.

We use erwin Data Modeler's automation capabilities, including APIs, scripting, Mart, or CI/CD integration regularly. Our automation system consumes the blueprint of data, then distributes it across multiple ecosystems to notify teams about upcoming changes, informing them that these modifications may impact downstream applications. This informs teams in advance, allowing them to address any necessary concerns because it provides enough time to ensure these changes do or do not impact systems, resulting in a smoother deployment process.

One feature that I wish erwin Data Modeler could improve is how user experience becomes much more intuitive, particularly when navigating large models or reviewing changes over time. Easier model exploration, clear visibility into differences between versioning, and practical documentation for use cases could help users work more effectively. I would also welcome the capability to have integrations with other platforms where the blueprint can be exchanged seamlessly without having to do an export and an import process. This would also help in bringing in the AI capability, which can assist in better positioning erwin Data Modeler not just as a tool for designing and documenting data models, but also as part of the broader data architecture and development lifecycle.

Regarding erwin Data Modeler's AI capabilities, there is an opportunity to enhance user experience from a governance standpoint, supporting tracking model changes, maintaining audit trails, and managing role-based access while enforcing consistent modeling standards across teams would be a value-added feature, particularly in a large-scale environment. It would also be beneficial to have better visibility into sensitive data attributes and their relationships to govern the requirements, including privacy and access controls. As organizations increasingly adopt AI development, integrating these governance and security considerations into the model and implementing workflows could also help teams work more effectively while maintaining appropriate oversights.

Regarding erwin Data Modeler's AI capabilities, there is still room for improvement regarding accuracy and reliability of output as large language models need clear instructions. There is room for growth because business requirements keep changing, relationships keep changing, and the capability to identify inconsistencies, missing relationships, or incorrect definitions are areas where it can improve. This is not to say that the current features are not good enough; they are excellent, but there is definitely room to catch up on AI changes. This is particularly important when modeling using blueprints inputted via AI assistant programs, and when an error occurs in the original model, it could carry over into a generated implementation. That is one of the experiences I faced when working on a project for the Adobe Experience Platform.

I experience some integration failures often with erwin Data Modeler, but there is documentation that we can look up that helps resolve these bugs. The new integration changes on our end sometimes pose a challenge, but the process is generally seamless, and the support available makes it much easier for us to troubleshoot any challenges we might face.

Managing data across different environments, such as traditional databases, NoSQL, big data, and cloud platforms with erwin Data Modeler presents the biggest challenge of translating from a traditional database to a non-SQL database. These challenges are primarily based on the structure of the data model. Overall, there are external tools that help us in translating that, and I would love to add this as a feature to simplify the process and reduce the complexity of using external extensions for making those changes or guiding through the process.

I have been using erwin Data Modeler for the past 12 months, and prior to that, I had been using it extensively for over three to four years when I was an associate technical architect.

erwin Data Modeler is stable.

erwin Data Modeler's scalability is good overall, allowing for seamless adoption across the organization in a larger ecosystem.

Customer support from erwin Data Modeler has been good so far. There can be some lag, but overall the experience is positive. I would rate the customer support received from erwin Data Modeler at an eight.

My team and I are planning to consider different solutions in the upcoming phase of our project, but we have not switched from a different solution yet.

Overall, I have seen a return on investment since adopting erwin Data Modeler, as our general improvement has resulted from fewer users needing the platform. As AI has been adopted on a larger scale across the enterprise, we reduced the number of licenses because the same number of experiences or features can be used from large language models, reducing costs for our company by close to 50%. This is why we are limiting user licenses for the ecosystem requiring erwin Data Modeler while upgrading our systems with AI agents that allow us to automate processes throughout the organization.

My experience with erwin Data Modeler's pricing, setup cost, and licensing has been good so far. While I would not call it a concern, the pricing tends to be on the higher end compared to competitors, yet it has good capabilities. However, there is room for improvement as other AI models can perform similar functions as erwin Data Modeler with more advanced visualizations. I would anticipate that there will have to be a reduction in costs in the upcoming six months considering the new AI modeling features introduced.

We are currently in the process of evaluating other options, as AI work agents are introducing new capabilities that need to be compatible with erwin Data Modeler's capabilities. This feature would greatly help erwin Data Modeler grow.

I would rate erwin Data Modeler around an 8.5 compared to other products. I chose the score of 8.5 for erwin Data Modeler because compared to other tools I have been using, it has a lot more features compared to those I have been comparing against. erwin Data Modeler's AI assistance has helped us improve our capability compared to other versioning models. My overall product rating for erwin Data Modeler is 8.5 out of 10.

We deploy erwin Data Modeler using a hybrid approach. We utilize an on-premise approach specifically because we are trying to build a POC for a financial sector company that has a specific use case. Additionally, we have a cloud-hosted approach where we are trying to do integration with the platform using connectors as we build connectors for other platforms from other companies to understand how these two systems can communicate with each other.

For our cloud-hosted approach, we use multiple cloud platforms. Our primary use case is AWS, depending on the clients that we operate upon. There are also cases where we utilize GCP as a solution for hosting as a cloud-based solution.

My advice to others looking into using erwin Data Modeler is that I highly recommend reviewing the product. It is one of the greatest products I have been using for over five years, and it has brought consistent improvements as AI has grown, seamlessly integrating new capabilities that can help customers advance alongside their evolving businesses.

Hybrid Cloud

Amazon Web Services (AWS)

My use case is mostly architectural design for our end-to-end deployment.

I like the best features mostly related to working with one of the Oracle tools that is OFSAA. When I make changes in the data model, even if the batch or any functionality resets it to zero, whatever changes I have done in the data model do not revert. It remains the same even if any database changes happen. No one can directly go and change the table structures or columns, therefore it is secure.

I see erwin Data Modeler has improved our data governance framework. My team mostly works as data modelers. When the model is ready for reporting or for the Power BI team, we give that end data to them for their further representation. We just create the model, replicate the functionality, and handle the logical aspects.

In erwin Data Modeler, areas that have room for improvement include performance improvements. I see that large models sometimes cause it to be slow when opening. Impact analysis can lag as well. Version control is another aspect. If we have developed a current version, it is not integrated with Git where we can easily compare different versions. UI modernization is also something we cannot utilize as it is primarily for development. Additionally, cloud-specific optimizations are needed when it comes to Snowflake or Databricks. When it comes to AI aspects, auto-suggestions for normalization or identifying primary, foreign, or surrogate keys are areas that can be improved.

I have been using erwin Data Modeler for more than four years and have significant experience with it.

Stability-wise, erwin Data Modeler scores an eight because it shows strong stability, especially in Mart repositories. While performance issues could occur with large models and improvements could be made, it remains reliable compared to other tools.

I rate erwin Data Modeler's scalability a seven. While it supports large enterprise models with multi-user collaborations effectively, performance can degrade during larger collaborations and requires tuning for optimal performance.

I rate erwin Data Modeler's technical support around seven or eight. This rating reflects my ability to effectively utilize the tool and get support for licensing issues, installation errors, or corrupted repositories end-to-end. Although complex issues may need several follow-ups, it still outshines other tools I have worked with.

Positive

I calculate return on investment based on total cost of ownership. If three engineers save ten hours each per month using erwin Data Modeler versus manual modeling, that equals three hundred sixty hours saved per year. The faster development and lower engineering cost lead people to adopt it. In larger organizations, the ROI tends to be more significant, and typical annual benefits can reach around $43,000 to $50,000.

Regarding pricing, it depends on the company. For a cloud or SaaS standard edition, it typically runs around two hundred to two hundred ninety-nine US dollars per month. For a workgroup edition, it comes to about three hundred ninety-nine dollars per month. Before purchasing, a free trial is available, and there are both term and perpetual licenses that allow for concurrent users. For larger teams leveraging a shared pool, it proves to be cost-effective.

I compare erwin Data Modeler with other solutions such as ER/Studio and believe it stands out as an enterprise-grade data modeling tool capable of serving different industries, such as finance, healthcare, and telecom. It supports cloud-native and hybrid warehouse integration, allows seamless creation of data catalogs or ELT/ETL pipelines, and can also be used for DevOps and CI/CD. Its visualization allows for documentation shared with stakeholders for better understanding, and it efficiently handles complex impact analysis without collapsing compared to other tools. It is also more trustworthy for integration and scalability.

My impression of the integration capabilities is positive. In a few of the projects, for instance, we connect the end reporting tool, such as Power BI.

I would describe the process of creating visual representations of data structures as having three types of data models: conceptual, logical, and physical data models. When I decide the design conceptually, logically I link how it has to be designed, and physically I actually create them. At that time, I perform the visual representation whether it is an entity relationship model or dimensional modeling or those types of things. Based on that category, I design the tables and columns and how the primary key and foreign key are linked to each other.

My impression of how effective erwin Data Modeler is in managing data across different environments is that mainly for RDBMS, I go for the entity relationship model. For document-based modeling, I work with MongoDB and NoSQL, and for dimensional modeling, I do work for Snowflake star schema for ETL-related tasks. Mostly these three types of modeling techniques I use.

My impression of the solution's role in enhancing collaboration among business, IT, and data teams is based on my overall nine years of experience. I started as a database developer, working with Oracle, SQL, PL/SQL, Unix, writing different stored procedures and functions for implementing banking functionality. As I move forward, whenever I design any table or database, I establish how the data relates to each other. This design and modeling part I do as a team with two to three others, and once the model is ready, we upload it into our front-end UI. Once it shows that the data model is successful, I check in our database whether the table or whatever items we have created have been reflected.

The main thing regarding how erwin Data Modeler helps ensure my data is AI ready and reliable is dependent on whether I have integrated that particular tool with any type of AI support initiatives or for AI/ML works. It can generate a starter data model or DDL script for me. If I have a GenAI script generation, it automatically generates the SQL and DDL script and validates it.

For deployment, I use erwin Data Modeler in the cloud primarily with Snowflake, and in Oracle, we have also migrated some tools there. Different object types are utilized across various parts. The cloud deployment is mainly through Amazon.

erwin Data Modeler can run in AWS environments and is mostly supported in a SaaS or cloud-based platform. It can also be installed on Windows server VM, container, or an EC2 instance and integrate with AWS databases such as Amazon Redshift, Aurora, or Athena via JDBC and metadata bridges.

When deploying erwin Data Modeler using AWS, it involves steps that can be easy or complex based on organizational needs such as security policies that do not allow SaaS. For self-hosted setups, I can easily launch an EC2 instance after logging into the AWS console, where I choose the server, storage, and subnet values, and install erwin Data Modeler while activating the license online, ultimately linking it to RDS or SQL server DB. Quest's hosted cloud offers a simpler deployment option without requiring infrastructure setups.

In my organization, approximately fifteen to twenty people work with erwin Data Modeler, with about seven to eight focused on data modeling. Multiple accounts and teams utilize it but in distinct ways, primarily for designing complex tables and presenting their connections to business stakeholders.

Maintaining this solution generally requires handling patches and version upgrades, which can be straightforward. However, database backups and user management within erwin Mart require regular attention.

For others looking into erwin Data Modeler, I recommend utilizing it based on my positive experience. Doing a proof of concept initially is beneficial, and focusing on data modeling fundamentals such as entity attributes and relationships will enhance usage. For beginners, it is crucial to gain practical knowledge in normalizing and naming conventions, while experienced users should focus on design and impact analysis.

I rate the effectiveness of the reporting capabilities for regulatory requirements highly, as we work with Basel II and Basel III concepts for regulatory reporting across different countries. Overall, I rate this solution a nine.

Public Cloud

Amazon Web Services (AWS)

My main use case for erwin Data Modeler is to build logical and physical data models by understanding the business processes and requirements, and I also create the physical and logical models from databases, which allows me to connect with multiple databases and create my models on the fly.

In my day-to-day work with erwin Data Modeler, I create or modify the logical and physical data models and then the corresponding DDL; for example, when attributes related to Genesis or any source system are updated in my model, I update the physical model in erwin Data Modeler and use forward engineering to generate the required alter table and DDL statements. Similarly, I use reverse engineering when the database already exists and I need to understand or validate its structure; for instance, when working with existing Silver Gold tables, I reverse engineer the database schema into erwin Data Modeler to bring in existing tables, columns, data types, keys, relationships, and so on.

The main use case of erwin Data Modeler is to design, document, standardize, and maintain an organization's data architecture from business requirements through to the physical database.

Using erwin Data Modeler has positively impacted my organization by improving data consistency and standardization, reducing development effort, enhancing data governance, fostering better collaboration, and minimizing risk and rework.

Since using erwin Data Modeler, I have noticed specific outcomes such as reduced modeling and implementation efforts, smoother forward engineering and DDL generation that has decreased the amount of manual SQL preparation, and faster model reviews. The visual modes and standardized naming conventions make it easier for architects, engineers, and business stakeholders to review and validate designs. Better reuse and maintainability from reusable domains and standardized model components reduce repetitive work, and overall project efficiency has increased. The timelines to deliver models and create business impact, including delivering Power BI dashboards, are reduced by 40 to 50%.

erwin Data Modeler helps ensure my data is AI-ready and reliable by providing consistent data definitions that standardize entities, attributes, and naming, which gives the AI and analytical team a more consistent understanding of what the data represents. It improves data quality and structure; well-defined logical and physical models help identify inconsistent data types, duplicate attributes, and other structural issues before data is consumed by AI or analytical workloads, laying a foundation for AI-ready data products.

erwin Data Modeler plays a significant role in strategic decision-making within my organization. It is not just a modeling tool but provides a foundation for informed decisions about data architecture and modernization, encompassing impact analysis, governance, and standardization, and fostering better communication with stakeholders. The visual models create a common language between business, architecture, engineering, and governance teams, which simplifies discussions around complex technical decisions.

The best features of erwin Data Modeler include maintaining naming conventions, definitions, domain, metadata, modeling standards, impact documentation, communication, impact analysis, and comparisons.

In my day-to-day data modeling work, erwin Data Modeler's naming standards and domain metadata features help maintain consistency and governance, especially when multiple data modelers are working across enterprise models; for example, a customer ID could be used at multiple places if not named properly, such as cust_ID, customer_ID, or customer_ID, which emphasizes the importance of standardizing naming conventions. Similarly, domains allow me to define reusable properties for commonly used attributes, such as a customer ID domain that specifies expected data type, length, nullability, and naming standards. By reducing inconsistency when a common attribute is used across multiple services and data models, standardizing the domain helps ensure that it has the same definition and technical characteristics wherever it appears.

Considering erwin Data Modeler's AI capabilities specifically for governance and security, stronger support for standardized naming conventions, domains, metadata, business definitions, and model documentation helps maintain an enterprise-wide model. Role-based access and control along with a model repository can help organizations manage who can view or modify enterprise models.

Regarding its AI capabilities for accuracy and reliability of output, I find it good for initial recommendations. AI can be useful for suggesting attributes, relationships, definitions, and initial model structures, as well as enriching metadata. It reduces manual efforts in documentation and repetitive work. However, I emphasize the importance of validating AI-generated relationships, keys, and cardinalities to ensure they align with business context.

The process of creating visual representations of data structures using erwin Data Modeler's enterprise-grade visualization feature is a structured visual way of translating business and technical requirements into understandable data architecture. It allows me to visualize relationships clearly, create focused subject areas, support reviews and governance, and maintain alignment with the database. erwin Data Modeler helps turn complex data structures into a visual blueprint that is easier to design, review, govern, communicate, and ultimately implement.

erwin Data Modeler is particularly effective for managing data across different environments such as traditional and cloud platforms. It excels in relational OLTP and OLAP environments with logical-physical modeling, keys, relationships, reverse engineering, forward engineering, and DDL creation. For cloud platforms, it has good capabilities, especially when modeling cloud data warehouses and enterprise data platforms, maintaining a consistent model and governance approach. In the realm of big data, it is useful for representing complex data structures and maintaining enterprise-level metadata. However, for NoSQL, there is room for improvement, as NoSQL systems do not always follow the entity-relationship patterns of relational databases, so richer and native support for document, key-value, graph, and other NoSQL patterns would add great value. The biggest advantage of having one go-to modeling approach across different environments is the positive impact it can create for the business, including connections with Databricks, Snowflake, Microsoft Fabric, and NoSQL data platforms.

The integration capabilities of erwin Data Modeler in connecting with erwin Data Intelligence and enterprise architecture tools provide great value. The data model foundations offer structural underpinnings while data intelligence augments broader metadata, lineage, discovery, and governance capabilities. Integration with enterprise architecture tools effectively connects detailed data models with higher-level architecture views and applications.

I would describe the transition between legacy systems and modern platforms using erwin Data Modeler as fairly seamless, particularly when it serves as the modeling and governance layer. It allows for reverse engineering of legacy systems, designing the target platform, model comparison, forward engineering, governance, and traceability. One area for improvement is providing more automated migration support for modern cloud and lakehouse architecture, such as automated data type mapping, transformation recommendations, and stronger lineage connection between legacy source fields and target cloud attributes. Overall, erwin Data Modeler provides a robust bridge between legacy and modern platforms, enabling reverse engineering of existing models, designing the desired target state, comparing the two, and forward engineering the necessary changes; the process would benefit from greater automation around cloud migration and transformation mapping.

I believe the first area where erwin Data Modeler could improve is in conceptual modeling; currently, there is no way to create a conceptual model. Additionally, large models can become difficult to work with, so better performance and faster model loading would help. If an AI assistant for modeling could be added, it could generate initial models from business requirements and suggest relationships and data types. Enhanced collaboration, version control, and improved real-time collaboration are also key areas for improvement, particularly for managing many-to-many relationships, which are not easily implemented. Lastly, modernizing the user experience to make it more intuitive and responsive would greatly enhance the UI.

Introducing AI could be a significant improvement as many-to-many relationships are not directly implemented in the physical model; they need to be resolved with an associative interaction or bridge entity when moving from a logical model to a physical database design. Additionally, if AI could assist in generating initial models and boost performance for large enterprise models, stronger cloud and modern data platform integration would also make erwin Data Modeler more useful in contemporary data engineering environments.

I have summarized all the points regarding improvements needed for erwin Data Modeler, and I do not have any new ideas at this moment.

I have been using erwin Data Modeler for over seven years.

I would describe erwin Data Modeler as generally stable and reliable for enterprise data modeling. It is particularly good for day-to-day relational modeling, reverse engineering, and forward engineering. Current peer reviews usually affirm its stability, although performance can decline with very large and complex models.

I would rate erwin Data Modeler's scalability as an 8 out of 10; while it scales well for enterprise modeling, very large models and highly distributed teams can expose some limitations.

I have clearly seen a return on investment using erwin Data Modeler, as it saves a lot of time compared to using Excel or other traditional methods, and it also aids in making business decisions by allowing me to explain insights effectively to business personnel who may not understand tables, columns, and rows but can visualize the erwin Data Modeler model.

I have seen a reduction of about 20% in the number of FTEs due to the efficiencies gained with erwin Data Modeler.

My experience with pricing, setup cost, and licensing for erwin Data Modeler has been seamless; as I work on an enterprise data model under a company license, I was provided a key and simply needed to install erwin Data Modeler using my company setup and start utilizing its services.

Before choosing erwin Data Modeler, I evaluated alternatives such as SAP, IBM, and Oracle SQL Developer Data Modeler; however, erwin Data Modeler stood out for its strong combination of enterprise-grade governance, documentation, reverse and forward engineering, and model management, offering more than just a primitive diagramming tool.

My advice for others looking to use erwin Data Modeler is to take your time; I would recommend it to organizations seeking mature enterprise data modeling as it offers significant value beyond basic diagramming tools such as Miro or draw.io. I suggest conducting a proof of concept first and evaluating its cloud integrations, performance, licensing costs, and collaboration capabilities against your specific requirements, especially if you are heavily invested in modern cloud, lakehouse, or NoSQL technologies. I rate erwin Data Modeler an 8 out of 10.

On-premises

Currently, I am an end-user of erwin Data Modeler, working as a full-time employee at TCS, not as a consultant. I interact with business customers and convert those concepts to developers. I occupy a middle position between the business and the developers.

My usual use cases for erwin Data Modeler involve converting the entire model from scratch, starting from the business concept through the conceptual model, logical model, and physical model. I handle relationship creation as well as subject area creation, and I perform all of these tasks in erwin Data Modeler.

The most valuable feature of erwin Data Modeler that I have found in my career is subject area creation. When I am joining tables in erwin Data Modeler, there can be more than 100 or 200 tables available, and they are joining with each other. This makes it difficult to understand the whole picture. However, if I segregate based on subject areas, that becomes the most valuable part. For example, if I define an ERP subject area, there may be sales involved with the ERP, manufacturing, point of sales, and HR. There are many models, so I prepare subject areas within the top of that model.

erwin Data Modeler plays a critical role in enhancing collaboration among business, IT, and data teams. Typically, analysts take information from the business and create system requirement and specification documents. My role is to convert the SRS into conceptual modeling and distribute it among developers. After that, the journey progresses from logical to physical. From the business concept through logical to physical, erwin Data Modeler depends on and covers every aspect within data modeling.

Areas of erwin Data Modeler that I think could be improved or enhanced include a few things. I have not worked in erwin for the past four to five years, but based on my 20 years of career experience, I worked with erwin one or two years on an earlier project and currently am working with it again. Based on my overall 20 years of career experience, I have worked with erwin modeling for four to five years. I am not a master of the tool, but I cannot identify significant flaws. The price should be reduced. That could be one area of improvement. Additionally, the licensing system could be enhanced. In our current project, there are five licenses within the business. If anyone is moving to erwin, the other person cannot enter the erwin solution. There should be a message indicating that five persons are involved in erwin, and you cannot use it at that time. However, that is a minor issue.

I have been working with erwin Data Modeler for four to five years. Based on my overall experience, which includes more than 20 years because I spent three or four years in business, my total business plus service experience is 23 years or more. As a modeler with this career span, I have worked four to five years with erwin Data Modeler. When I started, I transitioned from business into my career with Data Warehouse work. In the early 1990s, when Kimball's methodology was introduced in Data Warehouse technology, I began this journey. Overall, I have been working on Data Warehouse projects for 20 years.

My comment on how stable erwin Data Modeler is: I can rate it nine out of ten. It is the perfect modeling tool. In our current scenario, artificial intelligence has been introduced in many areas. I do not think erwin Data Modeler can be phased out from the data modeling perspective. Earlier, data modeling existed, and in the future, it will continue to exist. This should not be a concern.

Conceptually, erwin Data Modeler is quite scalable. The scalability question arises when we need to integrate anything that we are not getting from erwin Data Modeler. In that perspective, perhaps five to seven percent of integration is needed if we are not getting that feature from erwin Data Modeler. From the scalability perspective, it is good.

I have seen a return on investment when considering the cost and the results of the investment. If you are building a product, it is an investment of a company. Every product can be built with the help of the total structure of the modeling. If the modeling is compromised, then the entire structure will be compromised. This is very important for creating any type of data modeling. erwin Data Modeler is a primary part of the overall design. When I create a width of conceptual modeling, logical modeling, and physical modeling, there are many tables and objects available in the source, perhaps 1,000, 2,000, or 5,000. However, when creating a design in erwin Data Modeler or any type of modeling tool, approximately 80 percent of the tables can be removed. Actually, 20 percent of the objects can be determined as the whole lens of that project.

The process of creating visual representations of data structures using erwin Data Modeler's enterprise-grade visualization feature is straightforward. After completion of the model, I can easily export the model into a diagram. Features are available in erwin Data Modeler for this purpose.

My impression of the integration capabilities of erwin Data Modeler in connecting with erwin Data Intelligence and Enterprise Architecture tools is quite positive. Without erwin Data Modeler, I cannot create the data model on top of which the entire system depends. Without the data modeling tool, I cannot move forward. I have used the Enterprise Architect, Embarcadero Studio, and erwin Data Modeler. I have used most of the modeling tools available, but I found that erwin Data Modeler is the most suitable data modeling tool in our current industry. It is very expensive, but it is very good.

erwin Data Modeler is effective in managing data across different environments such as traditional relational databases, NoSQL, big data, and cloud platforms. In our current version, I have mostly used Databricks, SQL Server, and Oracle. I have used MongoDB, but not for the data modeling tools, so I do not have any practical concept about it. However, I have heard that the current version supports NoSQL. I have mostly used RDBMS for the modeling work.

erwin Data Modeler does help improve data governance frameworks. However, if you are asking about data governance, it is not limited to the erwin Data Modeler tool. We have to build most of the data governance components on top of it. If we want to secure the data in a proper way, we have to do data masking. If we want to secure the data, we have to implement proper access control. All of these elements are part of data governance.

erwin Data Modeler plays a significant role in strategic decision-making within TCS. Currently, I am working at TCS, and the models I have considered and created, as well as the data marts I have prepared, have made the customer very happy. As I mentioned earlier, without data modeling, no product can be built and no project can be built from scratch.

I can assess the effectiveness of erwin Data Modeler's reporting capabilities by saying that reporting capability means when I need to get any type of report, I can extract it from the menu options. That is not a problem. If I want to find the number of tables and the number of DDL scripts, I can easily extract this information. If I want to create or extract the data dictionary, I can extract it from there. That is not an issue. Many reports are available there. My overall rating for this review is nine out of ten.

I work for CVS with a pretty big database as a data modeler on the PBMs, the Caremark PBM side, which is the Pharmacy Benefits Manager. There's a retail side too for all the stores, but our data warehouse is for the PBM side of the business. Right now I'm working on moving all of our Teradata tables over to the cloud. We're redoing all the DDL from Teradata to BigQuery to GCP format, and we're rebuilding erwin Data Modeler files. As we're doing that, we're creating new erwin Data Modeler files in the GCP format for all the tables that we're migrating as we move them over. It's our only data modeling tool, so we use it for everything that we need it for.

I've used erwin Data Modeler for three major platforms: Oracle, Teradata, and now the cloud. I've never used it for a non-SQL database, just those three, and it works great for all three of them. I have another meeting right after this to talk about trying to get the next version of erwin Data Modeler, version 15, because I was told by someone at Quest that that one has full GCP BigQuery support. Right now, the version that we're using, 12.5, has support but it's not complete. There are some features that it's lacking, so I'm anxious to get the next version. Hopefully, this call will expedite that so that we can get a newer version of it that has more support for BigQuery. I've also used it to import Oracle SQL, Oracle DDL, and Teradata DDL, and then used a feature to convert that to GCP format, and that works pretty great. There are some things that it drops, but we have a workaround to get those things back, and I'll bet version 15 probably fixes some of that. But as far as it being able to work with those three database platforms and convert between them, it works really good.

I love the way erwin Data Modeler creates data models and presents them for our users. It's a great drawing tool and it represents the foreign key relationships, and you can easily drag those relationships to make the model easy to read. I think it's great. I've been using it a long time, and I think the way it presents the data in a visual format is great. It works fine, it's gotten better over the years, and it's really good now.

I can tell you that we're using it to manage CVS's data, which has some PHI and PII data, personal health information and personal identifiable information, which relates to HIPAA, the Privacy Act for medical information. We have to go through all of our tables, every column, and flag where it has PII data or PHI data and a couple of other flags that we use internally. I use the UDP feature, the user-defined property, for these extra data elements. erwin Data Modeler has a way that you can add identifiers for each column, and I use that feature a lot to track things that are specific to us, and we're using it for data governance. That's using an existing erwin Data Modeler feature to do that. There may be some data governance features built into erwin Data Modeler or some other related tool that we're not using, and if so, then I don't know about that. But I can tell you that we are using it to support our data governance needs, at least in a limited way using the UDPs for columns.

I write code and I write code accessing erwin Data Modeler's API, the Application Programming Interface, so that I can use Excel to update erwin Data Modeler and then get those updates and put them back into spreadsheets. The only thing I wish is that their API documentation was a little bit clearer and that it had better examples of successful code. To me, examples are worth a hundred pages of reference documentation. Just give me an example of how you use it, and that really helps. I know that's a nitpicking thing, but I wish their API documentation had a lot more examples of code that actually worked to do the things that they're talking about.

As far as erwin Data Modeler itself goes, when we have a lot of tables, I have some erwin Data Modeler files that have thousands of tables in them, up to 3,000 or 4,000 tables. When erwin Data Modeler files start getting that big, it takes a really long time, I'd say a minute or more, to open the file. Whenever I'm working in it, sometimes there are operations that also take a long time, so as erwin Data Modeler files get larger, the response time really slows down. An irritation is that every now and then, when I'm working and usually when I have multiple erwin Data Modeler files open at the same time, I get a glitch where a lot of the text fields get solid black, the first eight characters of column names might be blacked out with a rectangle. All I have to do is close erwin Data Modeler, shut it down, and bring it back up, and then everything is fine. It doesn't happen all the time, only occasionally, maybe once or twice a week, and those are the only two things I can say. The previous version of erwin Data Modeler used to crash unaccountably, but this one hasn't ever crashed on me, so it's been a lot more stable than the previous version that we had.

I started using erwin Data Modeler in 1998.

The previous version of erwin Data Modeler used to crash unaccountably, but this one hasn't ever crashed on me, so it's been a lot more stable than the previous version that we had.

If scalable means being able to handle more and more tables, then it just starts slowing down as we get more tables in an erwin Data Modeler file. It scales; you just have to be patient with it as it opens up those big files.

I've emailed support and they've always been responsive to me.

Positive

erwin Data Modeler was already there when I started; I don't do the deployment. CVS, a giant corporation, has a team that packages up applications and makes them available for all employees, and then another team that pushes them to your PC. I am not in that group that does the deployment of the tools, and I really never have been. It's always just been pushed to me on my laptop wherever I was, so I really can't answer that question. I assume it's not too hard, but I really don't know.

There is a corporate team above me that decides on what tools we use as a company. I don't know what they're doing for that or how they make those decisions, but we haven't integrated erwin Data Modeler with other enterprise architecture tools. It would be tough for me too because it's not my role to do that. I've recommended before that we could use it especially to integrate it with Informatica to get some data lineage going, but we haven't done it, unfortunately.

I haven't tried integrating erwin Data Modeler with other enterprise architecture tools, and there's a corporate team above me that decides on what tools we use as a company. I don't know what they're doing for that or how they make those decisions, but we haven't done it, and I haven't done it. It would be tough for me too because it's not my role to do that. I've recommended it before.

I rate the support team a 10. They've always been really helpful and prompt. I would rate erwin Data Modeler overall a nine. I'm sure there are some things that can always be fixed, but it's been a great tool. I've been using it most of my career, and my overall rating for this solution is 9 out of 10.

My main use case for erwin Data Modeler involves architectural and data modeling, as I use it to create conceptual, logical, or physical data models, which later help in designing and defining the relations between tables while documenting how data objects are connected. erwin Data Modeler is also useful for presenting the data structure clearly to both developers and business stakeholders.

One specific example of how I have used erwin Data Modeler for one of my projects is a recent scenario involving a very large data warehouse migration, where we were moving tables from Teradata to a cloud environment, GCP. We used erwin Data Modeler to create the data models in the new environment, mapped the tables and columns along with the relations between them, and generated the required DDL. This made the migration easier because we could visually validate the structure and relationships before implementing the changes in the database.

The best features of erwin Data Modeler include the ability to reverse engineer by importing database structures and DDL, perform forward engineering with DDL generation, and maintain collaboration through a shared model repository for our enterprise teams. Additionally, we can conduct impact analysis of the model to understand changes effectively.

erwin Data Modeler's collaboration and shared model repository has helped our team, which typically involves a few people working on the same models, by making collaboration easier. We can work with a common version of the model, review our changes, and ensure that the database structure stays aligned across the team, which is particularly helpful when developers need to validate the model against the actual database before implementing changes.

erwin Data Modeler has positively impacted my organization by providing our teams with a common visual representation of the data structure, which makes it easier to convey information to developers and stakeholders so they can understand the relationships, ultimately aiding in maintaining consistency across models.

I believe erwin Data Modeler could be improved in a few areas, particularly performance, especially when dealing with large models that contain thousands of tables, as opening those models and running impact analysis can become slow. Additionally, a more modern or user-friendly UI and better integration with Git for version control would enhance the experience, making version comparisons of model changes easier.

From my experience, erwin Data Modeler is strong from a governance and security perspective. However, there is room for improvement in its AI capabilities, such as automatically suggesting normalization or identifying primary, foreign, or surrogate keys to make modeling more efficient.

I have been using erwin Data Modeler for approximately three years.

The main issue with stability lies with performance, especially as very large models can take longer to open and respond.

erwin Data Modeler scales very well for enterprise-level data models and large teams; however, performance can degrade as the number of tables grows.

I have had a positive experience with customer support; whenever we reach out to them, they are very responsive and helpful, particularly with issues related to licensing, installation, and technical problems.

We did not have a specific alternate solution previously; erwin Data Modeler has been our primary data modeling tool for a long time, utilized across different databases and cloud environments.

Regarding pricing, setup cost, and licensing, the approximate pricing is between $200 and $300 a month, with standard SWA pricing around $200 to $300, and workgroup licensing at about $400. Larger user shared pools can potentially offer more cost-effectiveness, making $399 per month a cost-effective licensing option.

We have seen a positive return on our investment, having saved 10 hours per month, which roughly equates to 360 hours saved.

Before choosing erwin Data Modeler, we considered ER/Studio; however, erwin Data Modeler was already well established in our environment and provided the enterprise data modeling, documentation, and collaboration capabilities that we needed, which led us to continue using it.

My advice for others looking into using erwin Data Modeler is to start with a proof of concept, especially when working with large or complex data models, and ensure your team understands data modeling, normalization, and naming conventions. I also recommend evaluating performance with actual model sizes and checking necessary integrations, particularly for data lineage and version control, before making a final decision. I would rate this product a 9 out of 10.

Public Cloud

Amazon Web Services (AWS)

My main use case for erwin Data Modeler involves designing a logical and conceptual model, and physicalized model. I typically use it for creating tables, establishing relationships between tables, building new models, and performing end-to-end conversion such as conceptual to logical, logical to physicalization, and then physicalization to DDL. I understand the whole ER diagram.

A quick specific example of a project where I used erwin Data Modeler is based on healthcare, where I'm working on patient data. We have to design a patient record with the help of erwin, connecting patient encounters to the hospital. I designed a model using erwin to connect all the tables related to patient encounters, such as how the patient interacts with doctors and others, along with the diagnosis. I created this model with around 30 to 40 tables, establishing relationships, keys, primary and foreign keys, and generating a DDL based on that for deployment in production, which proves to be very helpful for my team, including data engineering and data analysts, to retrieve data from the tables.

I'm using erwin Data Modeler for designing tables and models from scratch and creating relationships. With erwin Data Modeler, it becomes easier for me to visualize tables and their connections. The subject area feature allows me to categorize my tables based on subject areas, such as selecting 'patient' for patient data. If needed for another client, I can choose that as well. erwin Data Modeler aids in visualizations and helps convert my conceptual model to logical, then logical to physical, and finally to DDL, while also allowing me to run queries on models that I recently built.

erwin Data Modeler has had a significant positive impact on my organization, as it enhances productivity. Without erwin Data Modeler, using other tools lacks features necessary for physicalizing logical models. If building a model takes one week without erwin Data Modeler, it reduces to about two days with it, showing its utility in streamlining our processes.

Regarding time saved, my past experience with erwin Data Modeler shows that building everything individually without it took significantly longer. I didn't track precise times, but my experience indicates erwin Data Modeler saves a lot of time by integrating a data dictionary with the model. The overall infrastructure simplifies making changes or adding details, and we also use ER 360 to help clients understand technical details at a business level.

erwin Data Modeler has been effective in enhancing collaboration among business, IT, and data teams. I've found it effective to work with multiple teams in my company, which has been seamless for me.

Among the best features erwin Data Modeler offers, I find that it tells us what each data element means, how it relates to other datasets, who owns it, and simplifies table creation and logical model design. We can effortlessly run queries on the erwin Data Modeler data model and visualize tables and relationships, which provides comprehensive descriptions of the attributes and entities used. We can extract all attributes and entities, putting descriptions under each to clarify their purposes.

The visibility of relationships and the ability to add descriptions to attributes and entities have greatly benefited me and my team. For new team members, lacking a description or data dictionary hinders understanding the model's purpose. With these resources, they can grasp the usability and enhance the tables. Without descriptions, they wouldn't understand how to join tables or their purposes, which is why a data dictionary is essential. Visibility of relationships is crucial, as it allows us to see the connections between tables—whether one-to-one, one-to-many, or many-to-many. Visualizing relationships simplifies understanding compared to just numbers or DDL. Observing the ERD clarifies the entire model, making the descriptions necessary for newcomers to understand the project better.

Regarding erwin Data Modeler's AI capabilities, I find its governance and security good. Access is controlled, ensuring only authorized users can view specific models, which enhances security. I'm just starting to explore the new version's AI features and look forward to their future developments.

The accuracy and reliability of outputs from erwin Data Modeler are quite good. It's widely used today for database architecture and data modeling, with recently upgraded features enhancing reliability and accuracy. Future versions are likely to build on these improvements.

Creating visual representations of data structures using erwin Data Modeler's enterprise-grade visualization feature is straightforward. New users may take some time to acclimate, but once familiarized with the ER diagram feature, designing models and visualizations becomes easy.

erwin Data Modeler effectively manages data across various environments, including traditional, NoSQL, big data, and cloud platforms. It focuses on model design first rather than data type, allowing universal data structures compatible across platforms.

The integration capabilities of erwin Data Modeler with erwin Data Intelligence and enterprise architecture tools are strong, allowing seamless connections and decision-making based on established relationships.

erwin Data Modeler aids in improving data governance frameworks by facilitating model designs that ensure compliance with governance standards.

erwin Data Modeler helps ensure my data is AI-ready and reliable by emphasizing data model design rather than direct data creation. When the structural design is accurate, the data inputted into that structure remains reliable.

The UI of erwin Data Modeler could be improved. It could be cleaner, as the current setup sometimes confuses users searching for specific features. Streamlining options and removing unnecessary elements could enhance its usability.

I find erwin Data Modeler's current state quite helpful, as it covers all aspects of data modeling. From designing models from scratch to implementing data vault techniques, the tool serves all needs. I believe it's already fulfilling requirements without needing improvements.

I rate it a 9 out of 10 because I'm not entirely satisfied with the UI; it could benefit from a cleanup and feature consolidation to improve navigation and reduce confusion.

I've been using erwin Data Modeler for the last three years, and currently, I'm using the updated, latest version of erwin Data Modeler.

erwin Data Modeler is very stable, and I anticipate more features will emerge in the future.

The scalability of erwin Data Modeler is effective, allowing for multiple chains, with more features anticipated in upcoming releases.

Customer support for erwin Data Modeler is excellent, providing easy access and quick issue resolution via calls.

I rate the customer support a perfect 10.

Previously, I used Lucidchart, but its limited options prompted my switch to erwin Data Modeler, which provides comprehensive features that eliminate the need for additional tools.

I've seen a return on investment through working on data models using fewer sources. Utilizing just one or two sources saves on costs, which are nearly 50% lower than before erwin Data Modeler.

My experience with erwin Data Modeler's pricing and setup costs shows it to be on the expensive side; the licensing fees reflect this higher cost, which results in fewer users adopting the tool.

Before choosing erwin Data Modeler, I didn't evaluate any other options; I simply read about erwin Data Modeler and made my decision.

erwin Data Modeler is deployed within my organization on a private cloud, allowing internal access only and enhancing security against outside intrusion.

We utilize multiple cloud providers for our private cloud deployment rather than relying on a single vendor due to diversity in services.

My advice to anyone considering erwin Data Modeler is to use it. While it may initially seem confusing, with time it becomes user-friendly and invaluable. I recommend it to anyone entering the fields of data modeling and architecture. I would rate erwin Data Modeler a nine out of ten due to its usability and features.

Private Cloud

Other

My use case for erwin Data Modeler is to identify the objects and keep the business details and object details into one pictorial diagram. Whenever a business wants to understand their operations, it is easy to walk through them with the PDF we have generated, which includes the links between the objects and functionality of the business and description of each column with business context. This tool is helpful for storing all this information.

My favorite feature of erwin Data Modeler is that we can export and import tables directly from the databases. It is easy to map from table to table and column to column.

In managing data across different environments, we are not dealing with the data in erwin Data Modeler. We use it to describe the relationships between tables and define how the data flows from one environment to another. For example, if we talk about a customer table, we define how many columns should be there and the data types and characteristics of that particular column and table. While we can store structure information at the mapping level, we are not managing data within this tool.

I would rate the visualization feature in erwin Data Modeler a six out of ten because the visualization is somewhat poor and some of the features are not user-friendly. You need to spend more time to make it a framework. There are areas where erwin Data Modeler can improve, such as scrolling and zooming in and out to see any particular columns. The visualization is average to above average, but not great.

Erwin Data Modeler could improve in areas such as the interface, as there are features like copy and paste, creating duplicates, and the visualization elements and toolbars which feel quite old. The user interface resembles a 1998 Windows structure, so that can be enhanced.

I have experienced issues with stability in erwin Data Modeler because it does not have an auto-save feature. Whenever I do something and it crashes, I have to start from scratch. There is no automation such as in Word or Excel, where documents auto-save, and if something happens, they start from where they got saved. This lack of an auto-save methodology can be improved so that if a system crash occurs, work can be saved and rework can be avoided.

Scalability in erwin Data Modeler is good as it connects with multiple databases to pull existing table structures. I believe we can read the table structure from any type of database.

I have not contacted the technical support or customer support of erwin Data Modeler. Every product I have worked with captures business frameworks and detailed information. We have not encountered situations requiring customer care support. Erwin Data Modeler requirements function at a standard level, and when we seek something higher, we connect with the team for support.

Neutral

The initial deployment of erwin Data Modeler was easy. It is not overly complex. Those who can understand how Windows functions can easily complete the setup.

Regarding pricing, I am unsure what the exact price is for erwin Data Modeler, but I believe it is not useful for an individual. It is more targeted toward an enterprise level since organizations looking to store business information and relationship values may consider the pricing. I have never thought about that because we never tried to buy it. It is typically for those who need this tool on an enterprise level.

I have not used any alternatives to erwin Data Modeler extensively. I have used Data Vault, but it follows a completely different methodology and does not compare to this tool.

I do not see any integration capabilities in erwin Data Modeler with any projects I have worked on. There are no particular environments such as development, testing, and production. Since there is no end product to place in production, we handle only one environment. The main purpose of integration would be if any output criteria exist. However, the output products are essentially for documentation purposes. So far, in the projects I have worked on, there has been no integration involved with this tool. My focus has been on documentation, which is metadata about the business strategy.

I do not recall how long it took to deploy erwin Data Modeler because there is no deployment involved in my projects. As mentioned, there is no end product for development and production activities. We are merely keeping the business information, and the application of this product serves that purpose. The end result is a PDF representing business data flow.

I am a user of erwin Data Modeler, and I do not have any partnership with them. Partnerships typically involve enterprise levels, but I am a customer. Whenever I start working with a client, they show me the tool, and I begin using it. I would rate my overall experience with erwin Data Modeler an eight out of ten.

My main use case for erwin Data Modeler varies, but I use it for forward engineering and reverse engineering and documenting databases.

I work with something that requires forward engineering or data reverse engineering or some other use case, and I start the tool up. I begin to enter data in or I acquire DDL to reverse engineer or I sometimes will use Excel to import data into the tool.

erwin Data Modeler offers a wide variety of databases supported both in the cloud and on premises, and support for IDF1X.

The support for a wide variety of databases and IDF1X helps me in my work by allowing me the flexibility to go from Oracle in a legacy system or DB2 to the cloud in a modern system for Snowflake. IDF1X is helpful because it is a standard and I know it pretty well, so it is highly useful in that context.

erwin Data Modeler has positively impacted my organization by having a team of modelers who are using it.

erwin Data Modeler could improve in a few ways, starting with the ability to not have to go out to the external Maven files to import and export the metadata from a different format. It could also add support for Oracle modeling techniques in addition to IDF1X, and finally, they could fix the views, as they do not use ANSI 92 views or ANSI 92 joins in the views.

I have been using erwin Data Modeler since 1996.

erwin Data Modeler is currently stable.

erwin Data Modeler's scalability is pretty good; the ability to create your own diagrams and figure out what is on each one makes it very scalable.

I have not dealt with customer support in two or three years.

I use whatever my employer uses and I have used other tools in the past, whatever the shop was using, I used.

The experience with pricing, setup cost, and licensing is that the licensing process is painful and quite onerous. The setup is okay, but there is room for improvement.

My company does not have a business relationship with the vendor other than being a customer.

erwin Data Modeler is worth it; it is important to work and coordinate with the end users and with the development team and build a common logical approach to how you will implement or your approach will be with a system, otherwise it gets skipped.

The experience with pricing, setup cost, and licensing is that the licensing process is painful and quite onerous. The setup is okay, but there is room for improvement.

At this place, the current organization, there are not really any alternate solutions, but I have looked at Embarcadero's ER/Studio, and that was pretty easy to use and fairly straightforward.

I would rate erwin Data Modeler's overall an eight on a scale of one to ten. I gave it an eight because I could not give it an 8.5, as there is room for improvement.

I would advise others looking into using erwin Data Modeler that it can be worth it if you can afford it.

I have not used the AI capabilities of erwin Data Modeler yet, so I cannot comment on its governance and security. Regarding the accuracy and reliability of output in erwin Data Modeler's AI capabilities, I also have not used the AI yet.

My overall review rating for erwin Data Modeler is eight.

I used erwin Data Modeler for approximately two to three years on a project. I used erwin Data Modeler for multi-level data modeling, conducting conceptual, logical, and physical data modeling within it. Conceptual modeling involved high-level entities and business terms and relationships. Logical data modeling included normalization and attribute-level specifications, as well as business rules. Physical data modeling involved data types, database-specific constructs, and schemas. erwin Data Modeler's ability to maintain model-to-model traceability is one of the biggest strengths.

The process of creating visual representations of data structures using erwin Data Modeler allows you to see everything you are doing. When you are working with the data, you are actually seeing the visual representation, and you have visuals to see what you are going to do and what has been done so far. It's not just attempting to make sense of a lot of data, but the way the data is organized and you have a visual of that organization helps a great deal in terms of trying to remember what you did and trying to retrieve the information. If you have worked through the entire process, it's a very significant advantage.

I think about the integration capabilities of erwin Data Modeler and connecting with erwin Data Intelligence and enterprise architecture tools. Model navigation was tree-based, it had a zoom pan, auto layout, entity-attribute filtering, views, and sub-diagrams. erwin Data Modeler was stable for most database engines, but I recall it became slow when the models were extremely large, containing more than a specific number of database objects.

In managing data across different environments, erwin Data Modeler supports most major relational databases such as Oracle, SQL Server, PostgreSQL, and MySQL. It can read metadata directly from live databases and can help visualize legacy systems or systems that are not properly documented. I think that erwin Data Modeler is a very reliable tool for modernization and migration projects.

Regarding stability, I have not seen any lagging, crashing, downtime, or any sort of instability; it was very stable for most of the database engines.

For the review, I would assign an eight because it has its own limitations. It has a very steep learning curve, limited support for NoSQL or streaming systems, and potentially a high licensing cost. I assume not many companies opt for erwin Data Modeler for their data modeling, and I do not know how dated the API is for erwin Data Modeler or whether it updates regularly to include all the upcoming technologies.

erwin Data Modeler became slow with large models, so we had to be mindful of not creating very large models within it. I do not have too many maintenance-related tasks.

For standardization, it was very good, such as creating naming conventions, data type domains, and rule enforcement to strengthen data quality. erwin Data Modeler had a very good standardization infrastructure and supported a controlled multi-user environment with check-ins and check-outs. It was good for hybrid as well as on-premise or cloud data ecosystems. When I say that it was good for cloud data ecosystems, I am assuming that it is definitely very scalable.

I did not try contacting their technical support; I used to contact my IT support instead.

Neutral

I have not used any alternatives to erwin Data Modeler and cannot compare it with anything else. In data modeling, I have not had too many projects to work on, and the other tool I used for data modeling was Lucidchart, which is very different from erwin Data Modeler and not even comparable.

I am not the right person to ask about the pricing of erwin Data Modeler because I was just a user. I would rate this product an eight overall.