dbt Reviews & Product Details

Honestly, I really like having all my SQL in front of me and tied to a Git repository. This way, I can actually see who made the changes. My space bar has stopped working on this computer, and I’m half asleep on the couch. So I’ll simply say that dbt’s testing functionality is what saves me. Since dbt can run independently in its own warehouse, separate from the business teams’ Looker dashboards, large-scale processing doesn’t impede their workflow. Additionally, we no longer have to manage different servers for transformation processes, which is good. Review collected by and hosted on G2.com.

In February 2022, a single corrupted webhook caused null values throughout our database, and hours later a generic “error” message in the dbt error log pointed to a problem in some obscure location. Also, if a developer uses a poorly constructed join and fails to monitor the warehouse size, our computing costs rise rapidly. My minor complaint is having to view massive YAML files for schema tests, where one misplaced space causes the entire test to fail. On top of that, dbt releases new versions frequently enough that it seems as though the documentation is always lagging behind. Review collected by and hosted on G2.com.

I simply love how I can write regular sql select statements and then dbt takes care of translating those into views and tables within bigquery w/o requiring constant supervision. After loading large amounts of data into the warehouse we use dbt to prepare those datasets for looker and tableau, and creating partitions or clusters in the config file is as simple as I could have hoped so our queries complete quickly. I've been working a crazy long shift today and I am literally falling asleep sitting here on the couch now and I don't think I would be able to write some sort of epic post... it's just easier to say it works. Review collected by and hosted on G2.com.

The cloud ui is annoying. When I'm looking at the result set of a query it defaults to cutting off rows and attempting to copy/paste results from the small browser window is a real pain. And on top of that, I'm stuck with a bad keyboard on this laptop and typing out all these repetitive yaml files has made me want to toss it in the trash. Compiling jinja errors also suck for debugging purposes since the error message is usually so vague that it won't even tell you which line caused the issue. Review collected by and hosted on G2.com.

dbt simplifies the process of building a solid data pipeline by offering a lot of features that would be difficult to implement from scratch. In particular, the SCD2 and incremental functionality helps remove a lot of overhead for developers and makes ongoing maintenance easier. There are also many other features that are great and contribute to a smoother overall workflow. Review collected by and hosted on G2.com.

There’s nothing I dislike about it, but I do have one suggestion:adding a feature for backfilling data (historical loads) will help a lot. Right now this can be done using a macro, but having an inbuilt option similar to incremental would make it much easier and help a lot. Review collected by and hosted on G2.com.

It’s very simple to use. All we really need to know is SQL, and it’s easy to create different types of materializations in it. Most importantly, it has a lineage feature. Review collected by and hosted on G2.com.

The UI could use a bit of improvement. After opening different models, it starts to look a little messy and cluttered. Cortex can be added by default to add models using AI. Review collected by and hosted on G2.com.

I find dbt very simple to use and get started with. Anyone with some SQL and YAML files can use it. It solves many problems related to data transformations, data quality, and data lineage. I like dbt Cloud for scheduling jobs, which is crucial for me, and I appreciate that I can schedule jobs in various environments based on my use case and get alerts too. I also enjoy the new AI feature, dbt-copilot, which helps with development, understanding code better, and writing dbt macros, which can be challenging. The initial setup was easy as it mostly required SQL expertise, and the training sessions were helpful. Overall, I love this tool. Review collected by and hosted on G2.com.

I like most use cases with dbt, but sometimes I have to write the entire code just to compile it. It can improve in terms of showing errors early before compile time. For example, if I am selecting from a model and a column is extra, dbt could flag it before compile and run actions. Review collected by and hosted on G2.com.

I have been using DBT every day in my project for the last two years, and I can say it is one of the best platforms for creating data models through SQL-based data transformations. DBT’s ease of integration has helped us save effort during code deployment. We integrated DBT with Git so we can work in parallel on multiple tasks across multiple models for a single requirement.

Its ease of implementation also makes it simpler to understand DBT’s benefits and has helped improve our efficiency. The command-line interface and the IDE environment make it easier for us to manage data and execute data models. DBT also has its own test case preparation features, which help us validate both our data model changes and the data being populated through DBT models. If we run into any product-related issues, there is a customer support team that is always ready to guide us through our blockers. Review collected by and hosted on G2.com.

In my experience, I worked with DBT for 2–3 years and noticed that, to use DBT effectively, you need prior knowledge of SQL and Python—especially if you want to take advantage of additional features like Jinja templates. This can be a major drawback for data analysts and developers who don’t already have a solid understanding of SQL and Python. Review collected by and hosted on G2.com.

I like best about dbt is how it brings a clean, developer‑friendly structure to analytics work. It makes modeling and transforming data feel organized and predictable, thanks to its simple SQL‑first approach and clear project layout. I also really appreciate how dbt encourages good engineering practices such as version control, testing, documentation. So the entire workflow becomes more reliable and collaborative. Review collected by and hosted on G2.com.

I dislike about dbt is that some parts of the workflow can feel a bit inflexible, especially when you're trying to customize how tests or models behave in more complex projects. It also relies heavily on command‑line and configuration files, which can become demanding as the project grows. On top of that, dbt doesn’t handle ingestion or real‑time needs, so user often need additional tools to complete the pipeline, which makes the setup feel less seamless. Review collected by and hosted on G2.com.

The way it handles large amounts of data, as well as how it integrates into AWS (S3/Glue) is great. This allows me to avoid building custom pipelines which would have been very time consuming and caused additional headaches and due to its columnar database design, all of my complex query requests are processed in a timely manner which means I do not fall asleep while waiting for results. Review collected by and hosted on G2.com.

Vacuuming Tables… seriously, I have to manually vacuum and analyze tables to keep this thing running smoothly? It looks like 2005. Managing the clusters and nodes is also a pain – it’s not true serverless. If you’re not paying close attention to the costs, they will jump up way too high. Review collected by and hosted on G2.com.

I primarily value how dbt shifts data transformation into a software engineering workflow. By materializing code into tables and views automatically, it lets our team focus on the transformation logic rather than DDL boilerplate. The model selection syntax is incredibly efficient for running specific segments of our DAG without wasting warehouse resources.

Macros and Jinja integration have also been game-changers for modularizing our logic and reducing code repetition. I find the YAML-based unit testing to be a robust way to ensure data integrity before it reaches our BI layer. Between the two, I prefer dbt Cloud over Core because the IDE provides immediate visibility into query results and schema changes, which speeds up our development cycle.

I use it every workday. Customer support was quick and responsive when I ran into issues during the initial setup of dbt with Snowflake authentication. Implementation was also straightforward when connecting it to Snowflake, and once the connection was established, I didn’t have any ongoing issues aside from needing to reauthenticate every day. Review collected by and hosted on G2.com.

The most significant friction point is the authentication lifecycle with Snowflake. The session tokens expire frequently (often every few hours), forcing a manual re-authentication process that disrupts the flow of development.

Additionally, there is a noticeable feature gap between the versions. dbt Core lacks the native, instant result-set previewing that makes dbt Cloud so productive. Bringing a similar "live preview" or integrated results pane to the Core CLI experience would make it a much more viable option for local development. Review collected by and hosted on G2.com.

I like the amount of variety that dbt offers because of the Jinja code and the inbuilt functions. The incremental models are very well built-in, offering lots of capabilities at a layer beyond what's in the data warehouse, like Redshift. The initial setup of dbt is very straightforward, which I find really helpful. Review collected by and hosted on G2.com.

I guess the development cycle of dbt is slower as a result. Writing YAML file descriptions and the actual code for every single model can lead to a slower development cycle. Review collected by and hosted on G2.com.