dbt is used for data transformation and data engineering with multiple data transformations and engineering functions. It is also used for orchestrating data engineering pipelines. An example of this is ingesting data from Azure Blob or S3 sources and then transforming it into different layers in the data platform.
dbt Platform
dbt LabsExternal reviews
194 reviews
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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.
Streamlined Data engineering and built-in lineages
What is our primary use case?
What is most valuable?
The best features of dbt include lineage and Jinja templating languages that make it easy for creating pipelines.
The built-in lineage feature provides a good understanding of the several layers where data is being loaded in dbt, allowing visibility from different layers into the end product.
dbt has positively impacted version controlling as it has different version control steps involved. The specific improvements seen with version control in dbt are that it has helped trace the data lineage, enabled faster trace and rollbacks, and enabled safe collaboration at every scale, which has improved data quality.
A return on investment has been seen from using dbt as the time has reduced while utilizing dbt in the form of data pipelines and ETL scripting. There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors.
What needs improvement?
dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section as it is not stable.
The copilot in dbt is not very comfortable for users, and my team has already tried using it but opted to move off from the dbt copilot to other copilots such as GitHub.
Improvement is needed in the tool itself in terms of the copilot, in terms of covering outages, in terms of testing, and in terms of quality reasons related to governance and collaboration.
For how long have I used the solution?
dbt has been used for about a year.
What do I think about the stability of the solution?
dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section. Overall, dbt is stable.
What do I think about the scalability of the solution?
In terms of scalability, dbt has improved the scalability of the organization depending on different dimensions for team size, data, and complexity of transformations.
How are customer service and support?
The customer support from dbt was good and was identified and resolved by the customer support team when reached out to.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
Initially, multiple solutions such as Talend Studio and Informatica were utilized for different projects before switching to dbt.
How was the initial setup?
The experience with pricing, setup cost, and licensing was that it was straightforward for the pricing setup and also on the licensing part for dbt.
What was our ROI?
A return on investment has been seen from using dbt as the time has reduced while utilizing dbt in the form of data pipelines and ETL scripting. There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors.
What's my experience with pricing, setup cost, and licensing?
dbt was purchased through the AWS Marketplace.
Which other solutions did I evaluate?
Before choosing dbt, other options were evaluated, but dbt was the preferred choice as it was an open-source solution that was already on the track.
What other advice do I have?
My advice to others looking into using dbt is that it is a good tool for having ETL or ELT transformations done. To begin with, a pilot project can be added with modular SQL or modeling, Git workflows, and a standardized project structure from source, staging, intermediate, to the mart layers, which will optimize performance. I would rate this solution a seven 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?
User-Friendly Data Modeling with Seamless Integration
What do you like best about the product?
DBT is great for organizing data models. It is user friendly, integrates well with other tools, and they had a great onboarding process.
What do you dislike about the product?
In dbt Cloud, I cant work on two different branches at the same time in different browsers.
What problems is the product solving and how is that benefiting you?
DBT allows us to take raw data from many sources and output it in clean, easy to use output tables that are used in our bi tool.
We finally found a solution for easier management of data models
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Dbt has resolved the problem of inaccurate or inconsistent transformations within our workflows. It has simple implementation and I use it frequently hence my usage frequency is also high. It has many features that can be used to test and keep track of the version that helps in uncovering errors at the earlier stages. It is lean cooperation throughout the team, reduced manual checks that have to be done multiple times, and ensures our data is reliable and can be used in reporting and business decisions.
Coding-focused data pipelines have accelerated delivery of in-house data products
What is our primary use case?
My main use case for dbt is for data transformation and data engineering.
A specific example of how I use dbt for data transformation and engineering is that we use it to connect and ingest data from our Azure blob and S3 buckets, then transform through our glorified serving layers into our data platform.
We use dbt to orchestrate our data engineering pipelines.
What is most valuable?
The best features dbt offers include built-in lineage, which is useful, and the Jinja templating language that makes it easy for creating pipelines.
The built-in lineage feature and the Jinja templating have made things easier for me because it's an easy language that most people pick up pretty quickly, and the ability to template SQL and generate SQL programmatically is useful.
dbt has positively impacted my organization by allowing us to create our data pipelines much faster, going from ingestion of data to creating a data product in weeks instead of months, and we can do it in-house with the skillset we already have.
I can share a specific outcome that resulted from using dbt: there was a data product a vendor did for us two years ago that took them six months to achieve, whereas we were able to do it in-house with dbt coupled with Snowflake in four to five weeks, which was much quicker.
What needs improvement?
dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub. Additionally, the debugging capabilities in dbt are practically nonexistent, making it very hard to troubleshoot and debug if you write incorrect Jinja code.
dbt is not as stable as I would prefer, as there have been a few outages this year.
For how long have I used the solution?
I have been using dbt for about four years.
What do I think about the scalability of the solution?
dbt's scalability is fine, and I have never had any capacity problems.
How are customer service and support?
The customer support is average.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I previously used a different solution; we used a bit of Talend and Wherescape Red.
What was our ROI?
I have seen a return on investment as it means we don't have to employ as many people; it's more about cost avoidance, but I have not removed employees.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing was simple enough.
What other advice do I have?
dbt is easy to use and easy to learn, but it has some limitations that I would love to see mitigated; however, in general, most of my engineers are happy using dbt.
My advice for others looking into using dbt is that it is good if you have an organization with engineers who prefer to code and get hands-on, but if you have teams of engineers who prefer a mouse-driven, drag-and-drop type, less technical coding environment, other tools might be more appropriate.
I rate dbt overall an eight 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?
Reliable data project workflow
What do you like best about the product?
What I like about dbt most is that my modeling work is much more comfortable. I spend my entire time modifying logic or verifying the changes and the set of features is what I actually need. I operate it with Snowflake, and that integration ensures that my updates are regular. The installation was relatively fast and the number of times the tool has been used demonstrates the extent to which it has come to my rescue in order to maintain projects and ensure they are well organized.
What do you dislike about the product?
The thing I do not like is that there are some spots that do not provide me with the flexibility that I need when I work on large portions of work. It imposes additional procedures which disrupt my rhythm. These weak spots are visible since I am in dbt so many times. There is a good response of the customer support but still the restrictions influence my speed during peak weeks.
What problems is the product solving and how is that benefiting you?
Dbt addresses our problem of disorganized model upkeep in the team. I apply it in my daily activities keeping track of the changes and updating and ensuring that all goes in the right direction. The size of the features suits very well into our workflow and the frequency of use demonstrates how much easier our process is now. It has assisted us in preventing the instances of miscommunication and enabled all of us to be more certain of the work we drive forward.
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