A Developer Friendly Transformation Tool
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
dbt solves the problem of scattered, inconsistent transformation logic by giving user a clean, structured way to manage SQL models, tests, and documentation in one place. I no longer needs to deal with random queries or unclear business rules, everything becomes version‑controlled and easy to trace. Which helps me in my workflows to become productive.
Reliable transformation practices at scale
What do you like best about the product?
One thing that I find impressive about dbt is that it promotes discipline in writing of transformations. It transformed my approach towards the way I deal with my work, as I now think twice before imposing changes. I use it on a regular basis, and it has enhanced teamwork since logic has less difficulty in reviewing and discussion. This has saved time on quick fixes and has assisted us in developing more confidence on outputs that may be shared.
What do you dislike about the product?
What I do not like about dbt is that there is a huge effect of little errors in the models. Some of them may break down under the pressure of having a few downstream pieces broken when there is a slight change. It is time consuming and can even bring several individuals into the same problem when it comes to debugging those chains. In my case, this retards progress and results in context switching which can be annoying when time lines are near.
What problems is the product solving and how is that benefiting you?
Dbt eliminates the issue of vague ownership and reasoning. It provides organization where responsibilities are clearly seen which enhances cooperation. In my case, it implies a reduced number of handoff problems and a streamlined collaboration. Co-workers become bolder in changes and tasks are less responsive on a daily basis. It has simplified our working process and made it more predictable in general.
Incremental data models have cut full refresh time and support trusted executive reporting
What is our primary use case?
I am currently working with dbt and Snowflake together. We use dbt for data transformation purposes. We obtain the data and store the raw data directly into Snowflake, then perform all transformations using dbt to prepare the data for reporting purposes.
We use dbt's modular SQL models. In dbt, we do not use full refresh or full data refresh. We have incremental strategies in place that only compute or transform incremental data, which operates in a CDC architecture. This approach is very fast, and we use it on a daily basis. We have scheduled all our dbt models using Airflow to run according to the scheduled time.
We use dbt's testing framework and the inbuilt functionality of dbt testing. For example, we use dbt's built-in tests to identify not null values and determine how many not null columns and values exist in each column. Beyond the built-in functionality, we have written custom SQL scripts to create external test cases on our models.
We ensure that incorrect or incomplete data does not go into the reporting layer because it can impact the business. We always perform dbt tests on our landing or raw data to ensure the correctness and completeness of the data before loading it into the final reporting layer. These reports are used by higher management, so we ensure that incorrect data is not published into the reporting layer for the Power BI reports.
We use dbt's documentation site generator. In dbt, we have YML file functionality, which can be used for creating documentation for each model. Whenever we make modifications to a model, we always update the YML file so we can track the history of how frequently we change the model. When new team members join, they can refer to this documentation to understand the data lineage and the data transformation strategy of the project.
What is most valuable?
dbt is very fast compared to the traditional tools. Previously, I worked on SSIS, which is provided by Microsoft, and data transformation took a considerable amount of time when dealing with large amounts of data. Since dbt works on the ELT architecture rather than the ETL architecture, it is much faster than traditional data transformation tools.
Previously, we were using SSIS packages, which were very slow. Recently we migrated all our SSIS packages to dbt models. After the migration, we moved the data from SQL Server to Snowflake. Previously, our data pipeline took around two days to load complete data when performing a full refresh. Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
What needs improvement?
I am not very familiar with dbt's version control system.
I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time.
In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.
For how long have I used the solution?
I have been working with dbt for the last three years.
What do I think about the stability of the solution?
I have not experienced any crashes, performance issues, or anything regarding stability and reliability.
What do I think about the scalability of the solution?
I find dbt very scalable.
How are customer service and support?
The dbt support team is very responsive. Whenever we have any issues on the dbt side, we always reach out to them. We did not face any challenges in the initial setup. I would rate the technical support a nine out of ten.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Previously, we were using SSIS packages, which were very slow. Recently we migrated all our SSIS packages to dbt models. After the migration, we moved the data from SQL Server to Snowflake. Previously, our data pipeline took around two days to load complete data when performing a full refresh. Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
The main reason we decided to switch to dbt is performance. As mentioned earlier, every quarter we perform a full refresh, and that refresh took considerable time on SQL Server. Since we had to migrate because our data is very large and growing daily, we adopted dbt because Snowflake is very fast. In Snowflake, the storage layer and the computation layer are separate, which is not present in the SQL Server traditional database. That is why we moved from SQL Server to Snowflake and from SSIS to dbt.
How was the initial setup?
We evaluated Databricks as well, but ultimately the client wanted to adopt Snowflake and dbt technologies only.
What about the implementation team?
We took help from Snowflake directly, the Snowflake company, for the Snowflake side. The dbt side is maintained or set up by our infrastructure team.
What was our ROI?
Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
What's my experience with pricing, setup cost, and licensing?
The pricing, setup cost, and licensing cost are managed by our infrastructure teams. As data engineers, we are not familiar with these details.
I need to check with my infrastructure team on whether we purchased dbt through the AWS Marketplace or directly from the local vendor.
Which other solutions did I evaluate?
Since dbt has a license cost, if a company is small and does not have much budget, they can explore other tools because there are other tools that provide the same functionality at a lower cost. If an organization is small, they can explore other products as well.
What other advice do I have?
I am currently working with Power BI, Tableau, Python, Databricks, Snowflake, and PySpark in the current project. I would rate my overall experience with dbt a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Speedy but however it is quite pricey and resource hungry
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
This has allowed us to move away from a pandas-based reporting solution, which crashed consistently. We now can process billions of records from our retail business and have a working dashboard. Its architecture provides separate storage and compute, allowing us to scale our compute resources as much as needed based on the demand for reports by management. Most importantly, it has reduced the amount of yelling from our data team regarding slow query performance.
Good performance in BigQuery, but the focus on monetization disappoints
What do you like best about the product?
dbt core for data transformations on BigQuery
What do you dislike about the product?
The change in the business model to focus on monetization has been notable.
What problems is the product solving and how is that benefiting you?
Generate data in a structured manner and with version control in ETLs is easy using SQL. This tool facilitates the handling and organization of information during the extraction, transformation, and loading processes.
Effortless Data Transformation with Easy Setup and Integration
What do you like best about the product?
DBT is a data building tool that is very easy to setup, to use and we are using it every day for our data transformation. It is very easy to integrate and leverage the tool with lots for features.
What do you dislike about the product?
Sometimes, it experiences server downtime.
What problems is the product solving and how is that benefiting you?
DBT is solving where our business analyst has data spread out in many different tables in our warehouse. Using DBT, I made datamart where I gathered all that information together so our BAs can get all the information they need from one single table.
Structured data workflows made effortless with dbt
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Before using dbt, our changes were far between and difficult to handle. At this point, all things go in the same way, which is advantageous to the entire team. The coordination between systems was eliminated through integration and implementation provided a sense of ownership. I can perceive fewer errors, more harmonious work, and a higher level of trust in products. It has made daily work less stressful and less value building oriented.
Reliable Data Automation and Trustworthy KPIs
What do you like best about the product?
What I appreciated most about DBT was its capability to automate the creation of form data models, allowing me to trust the data. I felt confident that the KPIs displayed were accurate, thanks to transformation logic that had been thoroughly tested and addressed, which I found particularly valuable.
What do you dislike about the product?
The learning curve could be smoother, and the user interface would benefit from some enhancements.
What problems is the product solving and how is that benefiting you?
My priority is to ensure that the strategic decisions I make are grounded in reliable and consistent data. DBT enables this by providing a column that transforms data into clear metrics, eliminating any mistrust in the data. This is achieved without requiring its own visualization, allowing the focus to remain on the quality of the data model. As a result, the agility and speed of reporting are significantly improved.
Streamlined Data Transformations with Room for Debugging Improvement
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
dbt provides a standard structure for our code base, eases data transformation with Jinja templating, organizes SQL scattered across tools, offers version control with Git, and includes data quality tests, making transformations maintainable and dependencies clear.
I can manage my own dependencies using dbt.
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
dbt allows us to scale the analytics engineering work so we are not running ad-hoc SQL scripts on a laptop. dbt separates the compute and storage logic, allowing us to define the "what", while it determines the "how". dbt automatically manages the dependency graphs, which is great, as I cannot handle tracking those manually.
