I am using a private cloud. I have used both on-prem and cloud versions of the product, but mainly the managed version, the on-cloud version. That is very convenient; of course with AI, that is being commoditized a little bit. But I like it; I used it more before. Now with AI, it is even easier to do documentation, but before AI, it was really convenient to generate documentation with that tool. My overall review rating for dbt is 7 out of 10.
In terms of metrics, I do not have exact metrics, but I get a sense of the speed of opening and closing data requests. I am not that familiar with the Scrum Master of our squad, but I believe our burn chart or something like that, which is an agile metric that measures the finished user stories, is the only sense or only kind of metrics that we have at the moment. However, you do get a sense of accomplishment and the speed of delivering value. I would say just the testing is something to focus on. dbt Fusion is something I am not completely aware of, but I need to try it because I think it is a great feature, especially because we are dealing with multiple models. For our use case, we are dealing with 50 plus, almost 100 models. Many models are running at the same time. If you add up all the compile time and parsing time, it can add up to quite a bit. dbt Fusion promises that the parsing is much faster in one-tenth of the time, I believe. I would say you really need to take care of your model and your data model because dbt gives you some freedom. If you do not really know what you are going to do, you can really mess things up. So you need to take care of the model, design your layers, define the responsibilities of each layer, define the criteria of each data layer, define the tests, and that is it. I would rate this product an eight out of ten.
Lead Software Engineer at a computer software company with 51-200 employees
Real User
Top 10
Feb 20, 2026
From a developer point of view, I find the ease of development and the code to be the most useful capabilities of dbt. I use VS Code to run the dbt models, and since the end user is only concerned about their output and reports, the ease of development and the fact that it is free are significant advantages. I assess the impact of dbt's version control system on team collaboration as great. I have used it extensively, especially when we had situations where the code broke, as we were able to roll back to earlier versions thanks to version control. I find dbt's documentation site generator to be quite crisp and straightforward. It helps with project transparency and onboarding new team members because the documentation is excellent for addressing issues we face. I learned dbt concepts primarily using their website and their tutorials, which helped me significantly compared to other platforms such as YouTube and Udemy. The course content that dbt provides is free and excellent for anyone starting out. dbt seems quite adequate currently, but if I needed to name a few areas for improvement, I would mention the migration of code to Git and GitHub, which sometimes fails and can be confusing for developers during handover. There are some glitches in the connection, but I am unsure if that is an issue from the dbt side or something else, so I cannot comment definitively. I would rate my overall experience with dbt at eight out of ten.
I am currently working with Power BI, Tableau, Python, Databricks, Snowflake, and PySpark in the current project. I would rate my overall experience with dbt a nine out of ten.
dbt is easy to use and easy to learn, but it has some limitations that I would love to see mitigated; however, in general, most of my engineers are happy using dbt.My advice for others looking into using dbt is that it is good if you have an organization with engineers who prefer to code and get hands-on, but if you have teams of engineers who prefer a mouse-driven, drag-and-drop type, less technical coding environment, other tools might be more appropriate. I rate dbt overall an eight out of ten.
I have had the opportunity to teach one of the tools to level entry engineers because it's easy to learn and easy to maintain. It's pretty useful. It depends on the architecture and the amount of company's data or the people that I'm going to advise. If you're starting a data engineering team and you don't have a lot of big data workflows, I would recommend Dbt. I recommend our tools for more advanced workflows but for starting, I recommend 100% Dbt. Overall, I rate the solution a nine out of ten.
dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.
Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it,...
I am using a private cloud. I have used both on-prem and cloud versions of the product, but mainly the managed version, the on-cloud version. That is very convenient; of course with AI, that is being commoditized a little bit. But I like it; I used it more before. Now with AI, it is even easier to do documentation, but before AI, it was really convenient to generate documentation with that tool. My overall review rating for dbt is 7 out of 10.
In terms of metrics, I do not have exact metrics, but I get a sense of the speed of opening and closing data requests. I am not that familiar with the Scrum Master of our squad, but I believe our burn chart or something like that, which is an agile metric that measures the finished user stories, is the only sense or only kind of metrics that we have at the moment. However, you do get a sense of accomplishment and the speed of delivering value. I would say just the testing is something to focus on. dbt Fusion is something I am not completely aware of, but I need to try it because I think it is a great feature, especially because we are dealing with multiple models. For our use case, we are dealing with 50 plus, almost 100 models. Many models are running at the same time. If you add up all the compile time and parsing time, it can add up to quite a bit. dbt Fusion promises that the parsing is much faster in one-tenth of the time, I believe. I would say you really need to take care of your model and your data model because dbt gives you some freedom. If you do not really know what you are going to do, you can really mess things up. So you need to take care of the model, design your layers, define the responsibilities of each layer, define the criteria of each data layer, define the tests, and that is it. I would rate this product an eight out of ten.
From a developer point of view, I find the ease of development and the code to be the most useful capabilities of dbt. I use VS Code to run the dbt models, and since the end user is only concerned about their output and reports, the ease of development and the fact that it is free are significant advantages. I assess the impact of dbt's version control system on team collaboration as great. I have used it extensively, especially when we had situations where the code broke, as we were able to roll back to earlier versions thanks to version control. I find dbt's documentation site generator to be quite crisp and straightforward. It helps with project transparency and onboarding new team members because the documentation is excellent for addressing issues we face. I learned dbt concepts primarily using their website and their tutorials, which helped me significantly compared to other platforms such as YouTube and Udemy. The course content that dbt provides is free and excellent for anyone starting out. dbt seems quite adequate currently, but if I needed to name a few areas for improvement, I would mention the migration of code to Git and GitHub, which sometimes fails and can be confusing for developers during handover. There are some glitches in the connection, but I am unsure if that is an issue from the dbt side or something else, so I cannot comment definitively. I would rate my overall experience with dbt at eight out of ten.
I am currently working with Power BI, Tableau, Python, Databricks, Snowflake, and PySpark in the current project. I would rate my overall experience with dbt a nine out of ten.
dbt is easy to use and easy to learn, but it has some limitations that I would love to see mitigated; however, in general, most of my engineers are happy using dbt.My advice for others looking into using dbt is that it is good if you have an organization with engineers who prefer to code and get hands-on, but if you have teams of engineers who prefer a mouse-driven, drag-and-drop type, less technical coding environment, other tools might be more appropriate. I rate dbt overall an eight out of ten.
I have had the opportunity to teach one of the tools to level entry engineers because it's easy to learn and easy to maintain. It's pretty useful. It depends on the architecture and the amount of company's data or the people that I'm going to advise. If you're starting a data engineering team and you don't have a lot of big data workflows, I would recommend Dbt. I recommend our tools for more advanced workflows but for starting, I recommend 100% Dbt. Overall, I rate the solution a nine out of ten.