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.
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.
DBT has absorbed all the stress while making my life a lot easier
What do you like best about the product?
I threw terabytes at DBT and expected the infrastructure to fail but DBT ran the distributed execution on its own with no intervention by me. The ability to run machine learning directly within SQL is strange but better than exporting to vertex.ai and dealing with cluster management myself. I also do not have to worry about cluster management as I can just write the query and wait for the results which in my opinion is very straightforward thing to do.
What do you dislike about the product?
Billing is a trap as well as if you run a generic query without a filter the costs jump up right away which can be very annoying. I had to re-write all of my stored procedures because the syntax isn't quite like pl/sql. And I really dislike reading the logs when a model fails and/or errors occur.
What problems is the product solving and how is that benefiting you?
DBT takes care of managing the server and I never had to manage it myself and also I used to have to sit around waiting for hours for my ETL to finish but with DBT now they complete really quickly. We just throw the dirty data into DBT and it will handle separating out the different types of storage without me worrying about it.
Streamlined Development and Reliable Data with Effortless DBT Orchestration
What do you like best about the product?
What I appreciated most was the elimination of duplicated code that used to be spread across various scripts. This change has significantly enhanced data reliability and now lets me implement business logic directly in pure SQL. I also value how much it accelerates development, and I find the orchestration and deployment with DBT to be exceptionally straightforward.
What do you dislike about the product?
I found the project management aspect challenging when dealing with hundreds of models, as the interface can at times be quite complicated.
What problems is the product solving and how is that benefiting you?
This tool has addressed our primary needs during the data transformation phase, enhancing data reliability and making development more efficient. It also acts as a central resource, ensuring that all teams use the same data management functions, even though it does have some shortcomings. Overall, DBT performs exceptionally well.
dbt keeps our data models clean, consistent and version controlled
What do you like best about the product?
I use dbt every day to transform raw data in our warehouse into clean, analytics ready tables and my workflow typically begins in VS Code, where I write sql models, then push them to Git for version control and run them through dbt Cloud. And overall it has also made collaboration between our team members much easier because dbt makes the whole process much more simpler.
What do you dislike about the product?
It's challenging when one change throws an entire run off track and the error messages are at best, vague. I also feel the need to defend is the handiwork of my contributor to dbt cloud. I have also encountered the overly relaxed strucure and the resulting chaotic command and environment specific configurations.
What problems is the product solving and how is that benefiting you?
I can onboard people more easily, which has dramatically increased the usage of my warehouse and decreased my reliance on fragile, one off sql scripts and we have a whole team of analysts, engineers, and product working to have the same versioned models just building and ready for use.
