dbt Reviews & Product Details

I threw terabytes at DBT and expected the infrastructure to fail but DBT ran the distributed execution on its own with no intervention by me. The ability to run machine learning directly within SQL is strange but better than exporting to vertex.ai and dealing with cluster management myself. I also do not have to worry about cluster management as I can just write the query and wait for the results which in my opinion is very straightforward thing to do. Review collected by and hosted on G2.com.

Billing is a trap as well as if you run a generic query without a filter the costs jump up right away which can be very annoying. I had to re-write all of my stored procedures because the syntax isn't quite like pl/sql. And I really dislike reading the logs when a model fails and/or errors occur. Review collected by and hosted on G2.com.

I use dbt every day to transform raw data in our warehouse into clean, analytics ready tables and my workflow typically begins in VS Code, where I write sql models, then push them to Git for version control and run them through dbt Cloud. And overall it has also made collaboration between our team members much easier because dbt makes the whole process much more simpler. Review collected by and hosted on G2.com.

It's challenging when one change throws an entire run off track and the error messages are at best, vague. I also feel the need to defend is the handiwork of my contributor to dbt cloud. I have also encountered the overly relaxed strucure and the resulting chaotic command and environment specific configurations. Review collected by and hosted on G2.com.

I use dbt daily to handle and transform data models in our warehouse. And it has also become the backbone of our analytics workflow and version controlled, transparent and a breeze to debug. I particularly appreciate how easy it is to modularize sql transformations and to document everything inline so the team knows Why each model does what it does and where this data came from. Review collected by and hosted on G2.com.

It can be a bit confusing to set up environment configurations especially when you are working on more than one project or warehouse. As your models grow and that framework layer deepens, your build time can start to slow down and it’s not always simple to identify the bottleneck. Also testing unconventional macros feels a little clumsy compared to writing tests for linear models. Review collected by and hosted on G2.com.

Thanks to dbt, I no longer have to depend on the engineering team to manage and transform the SQL data within our warehouse. It is the first step for me in organizing, testing, and documenting the entirety of our data models. I appreciate that all of this information is in one place in version control. I can track all changes made and the details surrounding each one. Review collected by and hosted on G2.com.

Troubleshooting complex dependencies and build errors can be a daunting task. There are occasions when a model fails and it is unclear which upstream change might be the cause. While the documentation is really good, I have found digging into a Stack Overflow or Slack thread to be the answer for some of the more obscure problems. I also find the visualization of lineage in dbt Cloud to be cumbersome. Review collected by and hosted on G2.com.

The largest benefit of dbt to me is that it provides structure to data work. I use it regularly with the BigQuery and version control tools. The integration is comfortable and teamwork is facilitated. It did not add any delay during implementation and the feature set enables one to reuse logic rather than rewriting it. It has minimized the number of errors and saved me time on the review and updates. Review collected by and hosted on G2.com.

The negative side about dbt is that it becomes rigid when projects expand. Minor modifications in some cases need more readjustments than anticipated, and this makes me slow down. The problems of debugging failures are not always evident, particularly to more novice team members and this has an impact on the speed of delivery. Clean source data is also used in implementation and hence when inputs are messy, it only adds more workload rather than making it easy. Review collected by and hosted on G2.com.

What I appreciated most was the elimination of duplicated code that used to be spread across various scripts. This change has significantly enhanced data reliability and now lets me implement business logic directly in pure SQL. I also value how much it accelerates development, and I find the orchestration and deployment with DBT to be exceptionally straightforward. Review collected by and hosted on G2.com.

I found the project management aspect challenging when dealing with hundreds of models, as the interface can at times be quite complicated. Review collected by and hosted on G2.com.

What I like most about dbt is that it brings software engineering best practices to SQL-based data transformations, making our SQL code base maintainable at scale. It has a clear model structure like staging, intermediate, and reporting layers. It provides macros and ref macros that make logic reusable, and the dependencies are really easy to understand. I appreciate its good collaboration with Git and integration with version control. Dbt has a strong documentation background, providing an auto-generated documentation site, so everyone is aware of what's happening in the project. The initial setup of dbt is really easy thanks to its great documentation, and it's available for almost all major data warehouses. Review collected by and hosted on G2.com.

One of the pain points is debugging and error troubleshooting. Error messages can really be vague, making it difficult to pinpoint which part of the core caused the failure. Also, large models are painful to debug. Query plan visibility inside dbt would be really helpful. Step by step execution for failed models would also be helpful. Review collected by and hosted on G2.com.

Every day I use dbt to convert raw data for it to be ready for analysis and I especially appreciate that it all involves only SQL and version control—no more messy scripts I like the feeling of writing simple queries and, at the same time, I enjoy the extra modularity and auto documentation. The tests and my transformations running concurrently provide me real confidence in the datasets I provide. Review collected by and hosted on G2.com.

When it comes to dbt, the learning curve is quite the challenge and it took me some time to figure out how to set the macros and organize the models in a tidy manner. The task of debugging is also quite a drag and since some of the error messages lack clarity, I end up spending a lot of time on logs. What is more, for large projects, the execution time can be rather long which can hinder the development flow. Review collected by and hosted on G2.com.

dbt runs well on Redshift, since that is what was mentioned over and over again in the notes; however, dbt simply compiles the SQL and the warehouse itself handles the heavy lifting. Using Git and Version Control for Data Models, is nice because it keeps the data model from exploding. dbt also integrates with our AWS infrastructure without requiring tears. The speed is sufficient, as it simply passes the work to the database; although, having the transformation logic in one location is helpful. Review collected by and hosted on G2.com.

The cost is becoming increasingly expensive and considering dbt is essentially a fancy SQL Compiler. dbt also has poor performance when handling un-structured data (although this may be due to Redshift); I'm unsure, everything seems to blend together. Additionally, the learning curve is very steep if you are not familiar with Jinja and setting-up YAML files properly. Review collected by and hosted on G2.com.

The interesting fact about dbt is that it simplifies the process of managing data pipelines. It was implemented successfully and I depend on it on a daily basis and hence my frequency of use is high. The amount of features such as model testing, documentation, and version control is especially appreciated by me. It has minimized errors in our conversion processes and has simplified the process of teamwork a lot and has helped the team maintain pipelines which are uniform and structured across projects. Review collected by and hosted on G2.com.

The thing I dislike with dbt is that it may be difficult to troubleshoot model errors. The features are good, and error messages are not always helpful in disclosing the problem. High frequency of use implies that such moments have the capacity of derailing workflows since I use it frequently. There is responsive customer support but edge-case fixes are not always immediately available, so the team occasionally has to check outputs before proceeding. Review collected by and hosted on G2.com.