We are still experimenting with testing, but not that much. We are not using some features yet. We are trying to introduce them because we are coming from a background of SSIS. The team used to work with SSIS, Microsoft SQL Server Integration Services. We are still adapting one feature at a time. Currently, we are working with the SQL modules and with the Jinja templating. We are experimenting with testing, but I would say towards the end of this year, we are planning to explore more of the documentation and the data lineage options as well. I would say the benefits are coming from GUI-based tools like SSIS. We have more control on the codebase. We can create something of a system where we can use macros and templating, speeding up the development cycle. We are now trying to introduce a little testing, and also we are using some sort of a CI/CD cycle, so continuous integration and continuous deployment. I do not believe that these kinds of features are that common as a package as a whole package. dbt excels in that area. I used to have a couple of notes about the performance, but lately I have discovered something called dbt Fusion, which, according to dbt Labs, they proclaim is much faster during the parsing of dbt models. However, I would love to see even more of an out-of-the-box solution regarding the testing. They are treating the testing in a good way so far, but I would love to see even more improvement because the whole data testing field is not very mature. It is not the same as software testing; for example, you have test suites, test tools, and profilers, but for data testing, it is not yet that advanced. I would love for dbt to take the lead on that.
Lead Software Engineer at a computer software company with 51-200 employees
Real User
Top 10
Feb 20, 2026
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 am not very familiar with dbt's version control system. I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time. In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.
dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub. Additionally, the debugging capabilities in dbt are practically nonexistent, making it very hard to troubleshoot and debug if you write incorrect Jinja code.dbt is not as stable as I would prefer, as there have been a few outages this year.
SQL statements that beyond DML, are not possible. Currently, they are not possible in Dbt. Having more features in SQL statements will support us. Another issue is the terms of data ingestion because Dbt requires sources to be defined, and you need to handle data ingestion with other tools. So having a data injection tool integrated within dbt will be awesome.
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,...
We are still experimenting with testing, but not that much. We are not using some features yet. We are trying to introduce them because we are coming from a background of SSIS. The team used to work with SSIS, Microsoft SQL Server Integration Services. We are still adapting one feature at a time. Currently, we are working with the SQL modules and with the Jinja templating. We are experimenting with testing, but I would say towards the end of this year, we are planning to explore more of the documentation and the data lineage options as well. I would say the benefits are coming from GUI-based tools like SSIS. We have more control on the codebase. We can create something of a system where we can use macros and templating, speeding up the development cycle. We are now trying to introduce a little testing, and also we are using some sort of a CI/CD cycle, so continuous integration and continuous deployment. I do not believe that these kinds of features are that common as a package as a whole package. dbt excels in that area. I used to have a couple of notes about the performance, but lately I have discovered something called dbt Fusion, which, according to dbt Labs, they proclaim is much faster during the parsing of dbt models. However, I would love to see even more of an out-of-the-box solution regarding the testing. They are treating the testing in a good way so far, but I would love to see even more improvement because the whole data testing field is not very mature. It is not the same as software testing; for example, you have test suites, test tools, and profilers, but for data testing, it is not yet that advanced. I would love for dbt to take the lead on that.
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 am not very familiar with dbt's version control system. I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time. In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.
dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub. Additionally, the debugging capabilities in dbt are practically nonexistent, making it very hard to troubleshoot and debug if you write incorrect Jinja code.dbt is not as stable as I would prefer, as there have been a few outages this year.
SQL statements that beyond DML, are not possible. Currently, they are not possible in Dbt. Having more features in SQL statements will support us. Another issue is the terms of data ingestion because Dbt requires sources to be defined, and you need to handle data ingestion with other tools. So having a data injection tool integrated within dbt will be awesome.