Having worked closely with data teams using dbt for the last year, I understand its benefits of a lightweight and version-friendly SQL work. This makes PG ideal for teams with multiple users because it simplifies code execution across different environments. Running functions in parallel leads to increased productivity, which is one of the most significant advantages. This is a versatile product that figures data-centric tasks by virtue of its robust features. The interface and usability of Dbt is commendable, making it very easy execute a complex data operation flow without any friction. Review collected by and hosted on G2.com.

On the flip side its advanced functionality can sometimes be a case of good things never used. It is quite difficult to leverage the maximum capabilities of this language, especially when you have a very limited number of examples of syntax which kind of makes it feel like there is some creativity limit on it preventing one from properly dwelling deep into exploring what its core functions are. Review collected by and hosted on G2.com.

I have been using dbt over more than a year now and I am using it almost every day as a data engineer. Its very simple to use and it is advantageous in many ways. It is easy to implement the code pipelines using dbt. The user interface is pretty good. Modularity and reusability is most useful feature. Also it is very useful in debugging with its code lineage feature. It is easy to integrate it with cloud data platforms such as Snowflake or Redshift. Also I have interacted with the support team from dbt for some of my doubts. I would recommend it to data architects to consider dbt in the modern data stack projects. Review collected by and hosted on G2.com.

It would be better if we have column level lineage available in dbt. Review collected by and hosted on G2.com.

DBT has been a game changer in the realm of data analytics for me. Its standout feature is the ability to transform data in the warehouse itself, making it lightning fast. The powerful modular SQL-based approach to defining transformations makes data engineers fall in love with it. Its automatic document generation feature is simply outstanding. Its SQL-based modular approach makes it easy for implementation. Review collected by and hosted on G2.com.

If someone is not well-versed in SQL, it will be difficult to implement it initially. The main feature it doesn't have is an inbuilt scheduler. The scheduler would make it a complete transformation tool for data engineers. Review collected by and hosted on G2.com.

Pipeline execution management is easy!

- Task dependencies are easy to manage

- Execution logs are detailed

- Alerting is easy

- Initial setup is super easy

Convenient dev environment

- The git integration enables to easily spin up development environments, check out a development branch and run your code in a production-like environment Review collected by and hosted on G2.com.

Would be nice to be able to set up several Snowflake connections for the same project. Review collected by and hosted on G2.com.

dbt is an efficient solution that is capable of transforming raw data into important insights. I've been utilizing it for data transformation and it integrates easily with most of the elt tools. It has tons of features that enhances the development experience. Review collected by and hosted on G2.com.

I've experienced issues when it comes to managing dependencies between models also realtime work isn't possible which is much needed. Review collected by and hosted on G2.com.

I am using DBT more than 1 year. Since the first day I started using it I like it's many things:

1) Simplicity: It is very simple. We just need to write SELECT queries to achieve the whole design. So after the development,

2) Version Control: DBT used to integrate with version control tool like GIT. So we can easily track the changes of it.

3) Materialization: With the help of the materialized, we can configure our model. We can create views, tables etc using it.

4) Documentation: In DBT we can create yml files to describe each of the models, columns and their usages and all. It will create very good DBT docs.

5) Data Lineage Graph: DBT automatically creates data lineage graph. It helps a lot to analyze and debug code.

6) Schedule: DBT has its inbuilt scheduler, so very easily we can schedule dbt jobs. Review collected by and hosted on G2.com.

dbt works in a batch mode. If we want to build a realtime job then it will not be possible in dbt. Although dbt is a data build tool. It does transformation but it is not exactly an ETL tool where we can do data extraction, transformation and load. Review collected by and hosted on G2.com.

To build reliable data models quickly and collaboratively Review collected by and hosted on G2.com.

It should be more accessible with more features Review collected by and hosted on G2.com.

Transparency for everyone, with dbt the task of working on data as a team is made easier. We can apply different processes in our analysis thanks to the ease that dbt provides, application of tests, governance and observability. Even though it is not a tool specifically for this, it helps to apply dataops Review collected by and hosted on G2.com.

Learning time, despite being quick to learn, requires time to better understand its features to speed up the development process Review collected by and hosted on G2.com.

It has helped us transform our data and structure it better and its easy use Review collected by and hosted on G2.com.

It should provide a tool to better enable model documentation. Review collected by and hosted on G2.com.

Model creation and CI/CD are top features.

Thanks to DBT we have reduced the inconsistency of our data models and we reduced the number of manual tasks performed by the team. Review collected by and hosted on G2.com.

The documenting process could be improved.

Since we started using DBT, the load on our data warehouse has skyrocketed. Tools that optimize queries would be greatly appreciated. Review collected by and hosted on G2.com.