dbt Platform

My main use case for dbt is data pipelines. I build data transformations and usually construct analytics pipelines.

I use dbt to handle transformations because it is really good at generating automatic documentation. dbt orchestrates and automates workflows, and it provides visualization of the data. dbt generates documentation from the code automatically. I also use tests where I can define automatic tests that will run.

dbt has positively impacted my organization by allowing us to expand the ceiling of complexity because once we have written the SQL, we can manage significantly more complexity since we are not spending all of our time doing it ourselves.

dbt can be improved by introducing Python. Ideally, I would want to be able to orchestrate across the DAG and have both Python and SQL combined. The last time I used it, it was not able to visualize that on the DAG.

The main issue I have had with dbt is that when I start a project inside dbt, the structure I have to use is somewhat strict. In terms of reusability and abstraction, it is really good inside a project, but as soon as I try to create a package, something that others could use or make my work reusable, it was not very good. dbt was not very good at package management.

I have been using dbt for four years.

dbt is stable.

dbt's scalability is probably where it shines the most; it is really scalable compared to using just SQL.

I have not had to use customer support for dbt. dbt has a very strong community. I would rate the customer support as really good since I have never had to use it; I can go on the forums and get most of my questions answered.

I did not previously use a different solution before dbt; every time I have used dbt, it was always embedded.

I have seen a return on investment from using dbt. I would say that you needed fewer people. You take your time, but I do not have concrete metrics.

My experience with pricing, setup cost, and licensing for dbt is that dbt is open source for its core modules, so the pricing, setup, and everything was really good.

I did not evaluate other options before choosing dbt.

My advice for others looking into using dbt is to read the documentation; it is not that difficult to use. I would rate this review an 8 out of 10.

In Power BI, I am currently creating solutions for this particular organization or team. I work end-to-end, providing the complete solution by understanding business requirements and KPIs, and building dashboards from end-to-end. This includes working with Fivetran, DBT, Python scripts, and other tools. I have been working with dbt for three years.

dbt is generally used with Jinja technology, and Jinja format is what I utilize. The structure of the scripts is different from other tools, and it is quite versatile, allowing me to use Python, SQL, or any other language. dbt mainly handles semi-structured data quite effectively, supporting major business transformations. dbt is used for transformation purposes, and I provide the business logic in the dbt scripts which run under the Git pipeline. Currently, due to cost cutting, we revised our technology strategy and created the pipeline with dbt for budget purposes. The database is loaded and business transformations are done through dbt, and it has a separate pipeline which loads the data into the database. We use Git or Bitbucket for versioning, and the code is stored there, with all business logic incorporation done within dbt.

dbt has reusable macros that can be created and used in multiple models, which I find very valuable. When I create the final tables, they are in the model folder, and under the model, these final tables are created. It also has a structured way of handling data, allowing me to mold it out effectively. A main feature is the ability to manage different pipelines, especially since I utilize Bitbucket for pipelining processes. In this, I can handle the scripts, determining which job should run based on various dependencies. I write loading processes in the .YML file, which I implement inside Bitbucket and in dbt scripts. The macros allow me to write multiple utilities or multiple scripts that can be reused in different models effectively.

The way dbt handles semi-structured data is by allowing me to easily manage any requirements or KPIs that come with it. I can handle it in the dbt scripts.

The initial setup of dbt is somewhat complex. Writing the scripts requires understanding Jinja technology, as the code writing structure is different compared to other tools, which can be challenging for developers unfamiliar with it. However, once I learned the structure, it became a robust tool for handling data, including semi-structured data like JSON.

dbt itself is quite extensive, and while many features are available, I often focus on common features. For unusual activities, I may not have enough experience to determine necessary changes or new features. Currently, I cannot suggest any changes or additions, primarily because I am working with structured data and not encountering many challenges with the dbt scripts. It successfully achieves our requirements.

The reliability of data in dbt is strong. When I conduct dbt tests, the data processed in the data warehouse performs exactly as expected. There are no interruptions during processing, ensuring consistency.

dbt is quite scalable since it has its own feature set for incorporating business logic, while the data storage occurs in Snowflake, allowing me to handle complex scenarios as needed effectively.

I have not had to communicate with dbt's technical support or customer service thus far, as my internal organization typically handles complex scenarios. If my DevOps teams are unable to resolve issues, I would consider reaching out, but that scenario has not arisen to date.

Regarding pricing, I am not deeply involved in that aspect. However, due to pricing increases, we have transitioned to using dbt pipelines for running our jobs. Fivetran was previously our tool, but after they raised their prices, we started using dbt at the beginning of 2025. Overall, I find dbt to be optimized compared to other tools.

In evaluating other solutions, we have our own data warehouse in Snowflake, where we can explore features such as Snowflake pipes for structured data. Additionally, I have worked with Teradata and manipulated data using temporary tables. Snowflake offers more features than Teradata, allowing coding in both Python and SQL, making it a versatile option alongside dbt for loading, storing, and processing data.

The initial setup of dbt is somewhat complex. Writing the scripts requires understanding Jinja technology, as the code writing structure is different compared to other tools, which can be challenging for developers unfamiliar with it. However, once I learned the structure, it became a robust tool for handling data, including semi-structured data like JSON.

Regarding pricing, I am not deeply involved in that aspect. However, due to pricing increases, we have transitioned to using dbt pipelines for running our jobs. Fivetran was previously our tool, but after they raised their prices, we started using dbt at the beginning of 2025. Overall, I find dbt to be optimized compared to other tools.

In evaluating other solutions, we have our own data warehouse in Snowflake, where we can explore features such as Snowflake pipes for structured data. Additionally, I have worked with Teradata and manipulated data using temporary tables. Snowflake offers more features than Teradata, allowing coding in both Python and SQL, making it a versatile option alongside dbt for loading, storing, and processing data.

dbt SQL model is what I create, and I develop different macros and utilities that handle various client databases effectively, as each client has a separate database but maintains the same table structure. For instance, I have created J&J_DWH for Johnson & Johnson, with most clients holding the same data structure, but there can be exceptions where certain clients might have fields missing. To manage this, I write checks in the dbt scripts so that if a specific column is not present for a client, the code does not stop. It takes measurable steps and creates a column with the same name but with null values. This utility is essential for handling complex scenarios across multiple clients.

The testing framework in dbt is useful, as I run dbt tests based on the number of clients, specifically running tests for a few clients based on their names. It generally runs unit test cases in the testing environment. The scripts I created validate successfully, and I can trace errors in the logs to identify any issues. In the .YML file, I document relationships, uniqueness tests, and other necessary details. Before final data loads, I run dbt tests to confirm that the data is accurately loaded into the table.

Regarding dbt's documentation site generator, it is extremely helpful for project transparency, particularly in complex scenarios. Organizations provide good documentation, and I refer to dbt.org to resolve issues or clarify doubts on activities I have not previously handled. This aids in ensuring data transparency and assurance during review processes with clients, enabling me to justify my methods based on the documentation and organizational standards. I would rate this review nine out of ten.