I am working with one of our enterprise customers, managing their newly established cloud warehouse. They are using Snowflake and we are using dbt to manage all the transformation and views and tables in Snowflake. I am not currently working with Cribl, but I used to work with it for almost three years. Currently, I am working with dbt and Snowflake stack.

dbt is a tool that is basically SQL and a little bit of Python, which is somewhat low entry-level, so many of the engineers can use it as well as the analysts. Multiple teams from the business side can use it as well if we allow them. Performance-wise, it mainly depends on the platform that hosts it, whether it is Snowflake or Databricks or BigQuery. There is not much complication. Of course, there are the benefits of having code, so you have a software development lifecycle; you can use version control, testing, and documentation.

I would say the best feature or the most desirable feature for dbt is the ability to write everything in code. It is treating data the same way that Ansible did or Terraform did for infrastructure as code. Now you can code the pipeline instead of using SSIS and Apache NiFi and even Informatica PowerCenter. All of these tools are GUI-based tools. They have a low entry barrier, but you cannot really integrate them in a CI/CD pipeline, for example. For dbt, we can create those. More recently with the advances in AI, LLMs, and code assistant agents, we can hugely leverage those in dbt because I can simply ask the agent to write the code or write the model. However, you cannot really ask them to draw any SSIS package or an Apache NiFi flow, for example.

I think that dbt helps us quite a bit because it exposes a little bit of the functionality of Snowflake directly to us. We can use it with ease because we have some experience with Snowflake and we know what controls to adjust. Because we are a team of multiple individuals, we need to collaborate. Without version control, you have to manage the whole codebase one feature at a time, but what we do is we can use branching and different feature branches. Each one of us is working on their own feature branch. We collaborate, we merge our changes, and we can roll back in case we introduced some bugs. I would say the version control feature is a huge bonus or a huge plus.

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.

I have been using dbt since September 2024, so almost a year and a half.

I think that one of the issues with dbt is upgrading to later versions because we have some functionalities that we have designed that overcome default behaviors for dbt. Every upgrade is a little bit of a risk for us because we do not know if the workarounds that we developed will be available for the next version. However, in terms of stability, we have had no issue.

I would say we have not experienced scalability issues so far. I am not aware of the scalability, but we are managing it on a very large scale. The bottlenecks that we have are not coming from dbt; they are coming from Snowflake. Once we scale up Snowflake, dbt has no issue whatsoever.

Besides the issue with the upgrades and the default behaviors for the macros that we overwrote, we did not really need to reach out to dbt support.

The team used to work with SSIS before I joined. They used to work with it, I believe, two or three years ago, but since I joined a year and a half ago, they switched to dbt. They switched to dbt just before I joined. I have also worked for some time with Apache NiFi as well.

I am not aware of the initial setup because they set up everything before I joined, but they are using dbt Cloud. I do not think there are many difficulties or any hurdles to overcome during setup. You simply link your dbt Cloud account with the Snowflake account and that is it.

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.

I have primarily used dbt for data transformation. My team and I work with source systems from various clients, some using Snowflake, others using SQL Server, and some using their own legacy source systems. We extract and load all the data into our data lake on Azure. From there, we use dbt to transform that data. In technical terms, we have extracted and loaded the data into our data lake, and from there we are doing the transformation with dbt. We are creating different layers such as silver, bronze, and gold to build a medallion architecture with dbt.

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 have been working with dbt for approximately seven to eight months.

Regarding stability and reliability, I see the tool as quite good. In terms of use case, market presence, demand, learning, and performance, I believe dbt will continue to be in the market.

I would rate dbt's stability and reliability at a minimum of eight out of ten based on the limited experience I have. Comparing it to tools I have seen in the past, such as Informatica and Alteryx, dbt can easily match up to that rating, specifically for stability.

I am not very certain how scalable dbt is from my experience, as I have had limited scope to work with it. I have not analyzed it deeply. I started as a developer and began with their free plan before moving to a paid plan, which was quite affordable at around one hundred or one hundred fifty dollars per month. We are currently focusing on report development and multi-tenant deployment, so we might consider scaling in the future.

Earlier, we used technical support for dbt, but that was only valid for a month or fifteen days. We later moved to the paid version because I was working on the proof of concept of Qlik Sense and other tools, and we finalized dbt as well. Initially, I explored dbt for free for about ten days without trialing any further support.

So far, we have not interacted much with technical support because we usually get help from the community on their website. If you type your question, you will likely find that someone has already asked it, so we do not need to contact their support directly.

I have not yet utilized dbt's testing framework.

My experience with the initial setup and deployment of dbt involved using VS Code. I am not very confident here because I received some help from another data engineer to set it up on my machine. However, I have used VS Code in the past, and with some libraries, it was successfully done, but I am not entirely certain about every detail.

I am both a customer and consultant for dbt because my company has bought the license, and as an experienced person, I work on a product for my company.

The course content that dbt provides is free and excellent for anyone starting out.

I have not practically used a different solution for the same use cases, but I have been part of teams that used tools such as Alteryx, Informatica, and Talend, even though I did not work with them hands-on.

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.