# dbt Reviews **Vendor:** Fivetran **Category:** [DataOps Platforms](https://www.g2.com/categories/dataops-platforms) **Average Rating:** 4.7/5.0 **Total Reviews:** 205 ## About dbt dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. ## dbt Pros & Cons **What users like:** - Users find **dbt's ease of use** exceptional, simplifying data transformation and integration processes significantly. (38 reviews) - Users value dbt for its **developer-friendly structure** , promoting organized analytics work and enhancing collaboration and efficiency. (22 reviews) - Users value how dbt offers **automation** in data modeling, enhancing reliability and confidence in analytics workflows. (19 reviews) - Users value the **clean and organized data transformation** approach of dbt, enhancing reliability and collaboration in analytics. (17 reviews) - Users value the **seamless integrations** of dbt with various data platforms, enhancing their modeling and transformation processes. (15 reviews) - Productivity Improvement (15 reviews) - Users value the **enhanced data quality** in dbt, benefiting from reliability, transparency, and streamlined development processes. (14 reviews) - Users value the **easy integrations** of dbt with various platforms, enhancing their transformation management and collaboration. (14 reviews) - Users appreciate the **easy setup** of dbt, finding it intuitive and highly integrable with various platforms. (14 reviews) - Solution Efficiency (14 reviews) **What users dislike:** - Users find the **limited functionality** of dbt frustrating, impacting flexibility and slowing down their workflow significantly. (14 reviews) - Users experience **dependency issues** with dbt, leading to complexity in troubleshooting and slower delivery of projects. (12 reviews) - Users find the **steep learning curve** of dbt challenging, particularly with complex projects and command-line reliance. (10 reviews) - Users find the **error messaging unclear** , making debugging and identifying issues a frustrating task. (9 reviews) - Users find the **error messages vague** , making it difficult to identify issues and troubleshoot effectively. (9 reviews) - Users find the **complexity of managing dependencies** in dbt to be challenging and sometimes frustrating. (8 reviews) - Users find the **complex setup** of dbt challenging, especially with confusing error messages and a steep learning curve. (8 reviews) - Debugging Issues (8 reviews) - Users find **feature limitations** in dbt, including challenges with setup, lack of scheduling, and complex templating. (8 reviews) - Users find the **learning curve quite challenging** , particularly with advanced features and debugging complexities in dbt. (8 reviews) ## dbt Reviews ### 1. dbt Streamlines Data Pipelines with Powerful Incremental and SCD2 Features **Rating:** 5.0/5.0 stars **Reviewed by:** Hithesh P. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 29, 2026 **What do you like best about dbt?** dbt simplifies the process of building a solid data pipeline by offering a lot of features that would be difficult to implement from scratch. In particular, the SCD2 and incremental functionality helps remove a lot of overhead for developers and makes ongoing maintenance easier. There are also many other features that are great and contribute to a smoother overall workflow. **What do you dislike about dbt?** There’s nothing I dislike about it, but I do have one suggestion:adding a feature for backfilling data (historical loads) will help a lot. Right now this can be done using a macro, but having an inbuilt option similar to incremental would make it much easier and help a lot. **What problems is dbt solving and how is that benefiting you?** DBT is like a framework for data engineering. Building ETL using a traditional approach is very time-consuming and error-prone things like dependency issues, documentation, and testing all require extra precautions and a lot of manual effort. That’s where dbt comes in as a lifesaver. It helps us build pipelines by providing features like lineage, auto-generated documentation, testing, macros, integrated Jinja, and more, which makes the overall process much easier to manage. ### 2. Effortless Data Transformations with dbt **Rating:** 5.0/5.0 stars **Reviewed by:** Akash M. | Senior Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** May 03, 2026 **What do you like best about dbt?** I find dbt very simple to use and get started with. Anyone with some SQL and YAML files can use it. It solves many problems related to data transformations, data quality, and data lineage. I like dbt Cloud for scheduling jobs, which is crucial for me, and I appreciate that I can schedule jobs in various environments based on my use case and get alerts too. I also enjoy the new AI feature, dbt-copilot, which helps with development, understanding code better, and writing dbt macros, which can be challenging. The initial setup was easy as it mostly required SQL expertise, and the training sessions were helpful. Overall, I love this tool. **What do you dislike about dbt?** I like most use cases with dbt, but sometimes I have to write the entire code just to compile it. It can improve in terms of showing errors early before compile time. For example, if I am selecting from a model and a column is extra, dbt could flag it before compile and run actions. **What problems is dbt solving and how is that benefiting you?** I use dbt for data transformations, quality testing, and lineage on top of warehouses. It simplifies starting up as it just needs SQL and YAML files. With dbt Cloud, I can schedule jobs and get alerts. The dbt-copilot aids development, particularly with writing dbt macros. ### 3. DBT is best platform for Data Transformation using SQL **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 07, 2025 **What do you like best about dbt?** I have been using DBT every day in my project for the last two years, and I can say it is one of the best platforms for creating data models through SQL-based data transformations. DBT’s ease of integration has helped us save effort during code deployment. We integrated DBT with Git so we can work in parallel on multiple tasks across multiple models for a single requirement. Its ease of implementation also makes it simpler to understand DBT’s benefits and has helped improve our efficiency. The command-line interface and the IDE environment make it easier for us to manage data and execute data models. DBT also has its own test case preparation features, which help us validate both our data model changes and the data being populated through DBT models. If we run into any product-related issues, there is a customer support team that is always ready to guide us through our blockers. **What do you dislike about dbt?** In my experience, I worked with DBT for 2–3 years and noticed that, to use DBT effectively, you need prior knowledge of SQL and Python—especially if you want to take advantage of additional features like Jinja templates. This can be a major drawback for data analysts and developers who don’t already have a solid understanding of SQL and Python. **What problems is dbt solving and how is that benefiting you?** For our project, DBT addressed our need for data transformation within our data warehouse. It helped our team manage and maintain data by using SQL-based transformations in the DBT data model. Its integration features also made it easier to integrate our work with GIT, so the team could collaborate in parallel and complete tasks within our timelines. In addition, DBT includes features for testing the data model and the underlying data, which helped us save time and effort. ### 4. A Developer Friendly Transformation Tool **Rating:** 4.0/5.0 stars **Reviewed by:** Syed A. | Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** February 03, 2026 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 5. Speedy but however it is quite pricey and resource hungry **Rating:** 4.5/5.0 stars **Reviewed by:** Joseph S. | Software Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 20, 2026 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 6. Versatile Data Transformation Tool **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about dbt?** I primarily value how dbt shifts data transformation into a software engineering workflow. By materializing code into tables and views automatically, it lets our team focus on the transformation logic rather than DDL boilerplate. The model selection syntax is incredibly efficient for running specific segments of our DAG without wasting warehouse resources. Macros and Jinja integration have also been game-changers for modularizing our logic and reducing code repetition. I find the YAML-based unit testing to be a robust way to ensure data integrity before it reaches our BI layer. Between the two, I prefer dbt Cloud over Core because the IDE provides immediate visibility into query results and schema changes, which speeds up our development cycle. I use it every workday. Customer support was quick and responsive when I ran into issues during the initial setup of dbt with Snowflake authentication. Implementation was also straightforward when connecting it to Snowflake, and once the connection was established, I didn’t have any ongoing issues aside from needing to reauthenticate every day. **What do you dislike about dbt?** The most significant friction point is the authentication lifecycle with Snowflake. The session tokens expire frequently (often every few hours), forcing a manual re-authentication process that disrupts the flow of development. Additionally, there is a noticeable feature gap between the versions. dbt Core lacks the native, instant result-set previewing that makes dbt Cloud so productive. Bringing a similar "live preview" or integrated results pane to the Core CLI experience would make it a much more viable option for local development. **What problems is dbt solving and how is that benefiting you?** We use dbt to manage the entire T (Transform) layer of our ELT process within Snowflake. Before dbt, our transformations were often opaque and difficult to test. Now, we have: Dryer Code: Macros help us maintain a "Don't Repeat Yourself" philosophy across hundreds of models. Improved Data Quality: By implementing automated tests on primary keys and relationship constraints, we catch upstream data issues before they impact stakeholders. Faster Onboarding: The combination of dbt Cloud’s UI and the structured project documentation makes it much easier for new analysts to understand our data lineage. ### 7. Streamlines Data Transformation with Best Practices **Rating:** 5.0/5.0 stars **Reviewed by:** Alexander C. | Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** May 05, 2026 **What do you like best about dbt?** I like the amount of variety that dbt offers because of the Jinja code and the inbuilt functions. The incremental models are very well built-in, offering lots of capabilities at a layer beyond what's in the data warehouse, like Redshift. The initial setup of dbt is very straightforward, which I find really helpful. **What do you dislike about dbt?** I guess the development cycle of dbt is slower as a result. Writing YAML file descriptions and the actual code for every single model can lead to a slower development cycle. **What problems is dbt solving and how is that benefiting you?** dbt allows us to handle data with software engineering best practices, letting us write tests, store code in a git repository, and manage it like a software project. ### 8. DBT has absorbed all the stress while making my life a lot easier **Rating:** 4.0/5.0 stars **Reviewed by:** Andrew S. | Business Development Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** November 30, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 9. Reliable transformation practices at scale **Rating:** 4.0/5.0 stars **Reviewed by:** Scott J. | Manager, data engineering and analytics, Logistics and Supply Chain, Small-Business (50 or fewer emp.) **Reviewed Date:** January 26, 2026 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 10. dbt keeps our data models clean, consistent and version controlled **Rating:** 4.0/5.0 stars **Reviewed by:** Bhupendra S. | Senior Team lead, Mid-Market (51-1000 emp.) **Reviewed Date:** November 03, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 11. Makes Transforming and Managing Data Models Way More Manageable **Rating:** 4.0/5.0 stars **Reviewed by:** Alexander V. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 12, 2025 **What do you like best about dbt?** Thanks to dbt, I no longer have to depend on the engineering team to manage and transform the SQL data within our warehouse. It is the first step for me in organizing, testing, and documenting the entirety of our data models. I appreciate that all of this information is in one place in version control. I can track all changes made and the details surrounding each one. **What do you dislike about dbt?** Troubleshooting complex dependencies and build errors can be a daunting task. There are occasions when a model fails and it is unclear which upstream change might be the cause. While the documentation is really good, I have found digging into a Stack Overflow or Slack thread to be the answer for some of the more obscure problems. I also find the visualization of lineage in dbt Cloud to be cumbersome. **What problems is dbt solving and how is that benefiting you?** Data transformations are far more efficient now with dbt. I no longer need to create custom scripts or deal with disorganized SQL in dashboards, as I can now have a single layer that is testable and maintained for all my transformations. It is quick and dependable to run models in dbt Cloud, which assures me that the data is consistent and current for our business teams. ### 12. Structured data workflows made effortless with dbt **Rating:** 4.5/5.0 stars **Reviewed by:** Josh K. | Analytics engineering lead, Architecture & Planning, Small-Business (50 or fewer emp.) **Reviewed Date:** December 21, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 13. Streamlined Development and Reliable Data with Effortless DBT Orchestration **Rating:** 4.5/5.0 stars **Reviewed by:** Florelle M. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** November 13, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 14. Streamlined Data Transformations with Room for Debugging Improvement **Rating:** 4.5/5.0 stars **Reviewed by:** Atharva P. | Cloud BI Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** December 15, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 15. dbt has become the backbone of my daily data workflows. **Rating:** 4.5/5.0 stars **Reviewed by:** Ivan O. | CEO, Mid-Market (51-1000 emp.) **Reviewed Date:** September 25, 2025 **What do you like best about dbt?** Every day I use dbt to convert raw data for it to be ready for analysis and I especially appreciate that it all involves only SQL and version control—no more messy scripts I like the feeling of writing simple queries and, at the same time, I enjoy the extra modularity and auto documentation. The tests and my transformations running concurrently provide me real confidence in the datasets I provide. **What do you dislike about dbt?** When it comes to dbt, the learning curve is quite the challenge and it took me some time to figure out how to set the macros and organize the models in a tidy manner. The task of debugging is also quite a drag and since some of the error messages lack clarity, I end up spending a lot of time on logs. What is more, for large projects, the execution time can be rather long which can hinder the development flow. **What problems is dbt solving and how is that benefiting you?** Finally dbt has solved the issue of maintaining the transformations within our team. No more custom code and ad-hoc scripts! I now have a single and unified and transparent process for building and managing pipelines and this has saved me hours and reduced the errors I make and given stakeholders more reliable data. I get to save time every single week! ### 16. I can manage my own dependencies using dbt. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** December 10, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 17. We finally found a solution for easier management of data models **Rating:** 4.5/5.0 stars **Reviewed by:** James M. | Business Intelligence Developer, Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** December 08, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 18. Efficient Data Management with Room for Documentation Improvement **Rating:** 3.5/5.0 stars **Reviewed by:** Mehdi N. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** November 04, 2025 **What do you like best about dbt?** I appreciate dbt for its secure practices in software engineering which I find crucial, particularly in ensuring integrity through data lineage, which plays a significant role in our security framework. The versatile templating system effectively enhances our data modularity, which amplifies the efficiency of our data processes. The intuitive templating also significantly improves the user experience by making our boards more operationally efficient. **What do you dislike about dbt?** I wish the error messages were clearer. Sometimes, it's hard to identify the root of issues based on the current messages. Additionally, the documentation could be more beginner-friendly, as new users might find it challenging to navigate and understand. **What problems is dbt solving and how is that benefiting you?** dbt resolves inconsistent data issues, making models easy to maintain. The templating boosts efficiency and data lineage ensures quality and security. ### 19. Reliable data project workflow **Rating:** 4.0/5.0 stars **Reviewed by:** Elia M. | Data transformation engineer, Accounting, Small-Business (50 or fewer emp.) **Reviewed Date:** November 30, 2025 **What do you like best about dbt?** 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 dbt?** 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 dbt 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. ### 20. Reliable Data Automation and Trustworthy KPIs **Rating:** 4.0/5.0 stars **Reviewed by:** Mohamed A. | General Management, Small-Business (50 or fewer emp.) **Reviewed Date:** December 15, 2025 **What do you like best about dbt?** 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 dbt?** The learning curve could be smoother, and the user interface would benefit from some enhancements. **What problems is dbt 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. ### 21. Effortless Data Transformation with Easy Setup and Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Jay P. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** December 26, 2025 **What do you like best about dbt?** 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 dbt?** Sometimes, it experiences server downtime. **What problems is dbt 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. ### 22. Best-in-Class SQL Transformations in dbt **Rating:** 4.5/5.0 stars **Reviewed by:** nayan S. | Manager Analytics , Mid-Market (51-1000 emp.) **Reviewed Date:** April 08, 2026 **What do you like best about dbt?** SQL Transformation is the best thing which dbt have. **What do you dislike about dbt?** Nothing as of now. but pricing seems high for model run **What problems is dbt solving and how is that benefiting you?** ETL and data engineering portion. We develop metrics and fact tables in dbt transformation ### 23. Easy-to-Use DBT for Version-Controlled SQL Models **Rating:** 4.0/5.0 stars **Reviewed by:** Dylan C. | RevOps Lead, Enterprise (> 1000 emp.) **Reviewed Date:** May 05, 2026 **What do you like best about dbt?** I use DBT for a lot of our sql models to version control. It's easy to use and helpful **What do you dislike about dbt?** So far there really hasnt been anything wrong with the platform. You need admin that know what they're doing **What problems is dbt solving and how is that benefiting you?** Data model version control and merging updates into snowflake tables on schedules ### 24. Fast, High-Performance Cloud Modeling **Rating:** 4.5/5.0 stars **Reviewed by:** Olena C. | Analytics Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** April 24, 2026 **What do you like best about dbt?** speed of building models, performance, cloud based **What do you dislike about dbt?** a steep learning curve for non-technical users, high resource consumption with large datasets, and lack of built-in scheduling. **What problems is dbt solving and how is that benefiting you?** dbt (data build tool) solves the critical problem of inefficient, siloed, and unverified data transformation by allowing data analysts and engineers to transform data within their warehouse using SQL ### 25. If its worth it, a data transformer with amazing features **Rating:** 4.5/5.0 stars **Reviewed by:** Ali A. | Analytics Engineer / BI Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** October 01, 2025 **What do you like best about dbt?** I appreciate how this tool brings software engineering principles to our data collection process, making it more scalable, auditable, and reliable. I also really enjoy the ability to write straightforward tests that execute automatically. **What do you dislike about dbt?** At times, handling very complex transformations or preprocessing tasks requires the use of more advanced Python packages, which means I often need to rely on external solutions. **What problems is dbt solving and how is that benefiting you?** At this stage, we are able to use it to reliably scale our business metrics, which provides us with greater speed and is transforming our data stack due to its robust operations and data preparation features. Integration with DBT is seamless, making the entire process smooth. ### 26. Powerful Features and Git Integration, but Error Messages Need Improvement **Rating:** 4.5/5.0 stars **Reviewed by:** Marissa M. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** October 27, 2025 **What do you like best about dbt?** DBT provides valuable features for rapid software development, and what I appreciate most is its seamless integration with Git. Its modular design, which includes the ability to build reusable models, conduct code reviews, and generate automatic documentation, is excellent. Additionally, I am impressed by its capability to define and run quality tests, which further enhances the development process. **What do you dislike about dbt?** When a DBT run fails, the error messages displayed in the terminal are quite generic. I find this frustrating because it means I have to spend extra time searching through the logs to figure out what went wrong. **What problems is dbt solving and how is that benefiting you?** It solves many problems, especially in terms of data trust, and improves integration, The method runs seamlessly and speeds up processes. We went from hundreds of disorganized scripts to a logical and maintainable pipeline structure. ### 27. We finally brought order to complex metric definitions **Rating:** 4.0/5.0 stars **Reviewed by:** Daniele B. | Data Health Engineer, Management Consulting, Small-Business (50 or fewer emp.) **Reviewed Date:** September 05, 2025 **What do you like best about dbt?** It is very nice that dbt gives us the capability to write out our transformation logic as application code. The process of integrating it with Git implies that our whole team may contribute to data models and track changes without hassles, and revert changes as well. Even the separation of the development and production environment out of the box is a tremendous advantage, so we can ensure that we are testing new things without necessarily influencing live reports. Lastly, the automated documentation generation process has made the data models so simple to comprehend, even to the business users, without ever having the touch a SQL. **What do you dislike about dbt?** The initial set up of dbt can be a little fiddly at times and with a mix of operating systems and local database connections. It also lacks its own scheduler, so still we will require an external orchestration tool to carry out our data pipelines. The more advanced Jinja templating and macros proved to be more cumbersome to our team to learn than the simpler SQL. **What problems is dbt solving and how is that benefiting you?** Our greatest weakness prior to dbt was inconsistent reporting amongst departments, this contributed to lack of numbers and big debates on the correct numbers between departments. Marketing could report a figure of new customer acquisition and sales could report an entirely different figure and that could slow down decision making. I we have one version controlled definition of all of our critical business metrics since dbt is in place. Such change came to mean that our leadership team trusts the figures implicitly and no longer spends so much time on reconciliation of numbers, but can actually analyze them. ### 28. DBT - From data science to product strategy **Rating:** 4.5/5.0 stars **Reviewed by:** Masha P. | Senior Product Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** September 14, 2025 **What do you like best about dbt?** I liked that it completely the way my team and I interact with information. With DBT, I know our customers, usage metrics are accurate and consistent, and its fantastic because it prioritizes functionality to validate our product hypotheses. I loved DBT because it relies on reliable data to make decisions. **What do you dislike about dbt?** For team members unfamiliar with engineering workflows, doing so could be challenging. **What problems is dbt solving and how is that benefiting you?** Its very useful and benefits us a lot because it takes data-driven product development seriously, and for me its great because it provides reliable analytics capabilities, and honestly, dbt is a worthwhile strategic investment. ### 29. Good performance in BigQuery, but the focus on monetization disappoints **Rating:** 2.5/5.0 stars **Reviewed by:** Duvan Dario D. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 06, 2026 **What do you like best about dbt?** dbt core for data transformations on BigQuery **What do you dislike about dbt?** The change in the business model to focus on monetization has been notable. **What problems is dbt 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. ### 30. Great tool to easily transform documents and take advantage of their functions **Rating:** 5.0/5.0 stars **Reviewed by:** Eduardo V. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 18, 2025 **What do you like best about dbt?** What I liked most was being able to transform models, which is easy with DBT because its been extremely good and useful, with understandable documentation, and it speed up development time. The documentation is interactive , managed, and essential. It works well now. I like most of its features. **What do you dislike about dbt?** The only issue I had a while back was that we received an error with no error code, which was difficult to troubleshoot because it wanst very informative. **What problems is dbt solving and how is that benefiting you?** DBT does a great job solving problems in creating collaborative data models, bit data analytics, and data quality, which is beneficial for us, too, because of their ETL tools. Once you understand them, everything works together, making the job easier. ### 31. DBT is an incredibly powerful, versatile, and inexpensive ETL tool **Rating:** 5.0/5.0 stars **Reviewed by:** Andy M. | Business Intelligence Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** June 26, 2025 **What do you like best about dbt?** It is inexpensive to use the DBT cloud service. DBT itself is free for a self-hosted environment. Very easy to set up local dev/test. Quite intuitive to use. Flexibility of macros, functions, Jinja, etc. Easily integratable with Snowflake, Clickhouse, Postgres, etc. I've never had to reach out to DBT's customer support even once in the 5+ years I've been using it. **What do you dislike about dbt?** I wish that more Jinja syntax was supported (like Ansible). I also wish that more functionality was default in the product (e.g. auto-generated YAML schemas). I was really excited about the semantic layer functionality that DBT released but it seems like the final product was a disappointment and it doesn't have some of the functionality that other layers have like symmetric aggregates. **What problems is dbt solving and how is that benefiting you?** Building ETL/ELT pipelines, or building a reusable ETL/ETL tool, on my own would take a long time. Conversely, buying a solution would also tend to be quite expensive and most providers don't open source their code so I wouldn't be able to self-host if it made economic sense. DBT is both easy to set up and inexpensive, so it really beats all the other options ### 32. Transforms SQL with Engineering Principles, But Steep Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** James B. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** October 19, 2025 **What do you like best about dbt?** I really appreciate how DBT brings software engineering principles to SQL, transforming our SQL into a dependable data model. This helps resolve reporting errors that would otherwise consume a significant amount of our IT team's time. **What do you dislike about dbt?** I didt really like that it requires mastering concepts like Jinja and Git. **What problems is dbt solving and how is that benefiting you?** By centralizing data transformation logic and raising it to a higher level, this tool becomes essential for any IT team managing a data warehouse. It enables teams to move beyond just generating information, allowing them to create scalable, high-quality data products. ### 33. DBT excellent data collaborative modeling software **Rating:** 5.0/5.0 stars **Reviewed by:** Kathryn M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 13, 2025 **What do you like best about dbt?** I like to simplify the transformation of the data and take advantage of SQL and Python in DBT in addition their skills are ideal for our needs. Finally I liked it to be relatively economical to compare other similar tools. **What do you dislike about dbt?** The negative for me is that sometimes the analytical development process can slow down a bit. **What problems is dbt solving and how is that benefiting you?** It is very beneficial and if it solves the problems in the projects and the analysis of large amounts of data and the transformation of the SQL based data and ends up being useful for our equipment and really works well and I hope it is maintained like this. ### 34. Provides excellent insight into the various sources and streams of data **Rating:** 5.0/5.0 stars **Reviewed by:** Kevin E. | Director Of Data, Small-Business (50 or fewer emp.) **Reviewed Date:** July 28, 2025 **What do you like best about dbt?** I like that it has error-detection capabilities and offers a wide range of connections. It makes it easy to sync analytics data and has a simple user interface that makes data management easy. I was able to easily navigate through the pages using the side panel. **What do you dislike about dbt?** I'm satisfied with the platform, I haven't encountered any issues yet. The team has been excellent with us, they've developed an impressive management tool. **What problems is dbt solving and how is that benefiting you?** It has allowed us to bring the most relevant data, offering a simple way to securely transfer data. We can obtain important product information, which has helped me save time and effort. I have had an excellent experience with this platform. ### 35. DBT at my point of view **Rating:** 4.0/5.0 stars **Reviewed by:** Anandhakumar R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 28, 2025 **What do you like best about dbt?** Data transformation logic can be expressed in SQL Data can be transformed in batch Ease to use, it has lot of good features similar in Django web application **What do you dislike about dbt?** Joining multiple database types is not possible. Ie., combining two databases like oracle and mysql. Persistent cluster is required for running the sql statements. Like Presto/Hive it can’t be connected to BI Directly **What problems is dbt solving and how is that benefiting you?** Enables engineers to transform data in their data lakehouse using select statements in SQL. Converts SQL select statements into Tables or Views Supports DW process such as incremental, SCD etc., Graphical representation of pipelines ### 36. Facilitates the implementation of analytical code quickly and easily **Rating:** 5.0/5.0 stars **Reviewed by:** Ali M. | Senior Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** July 24, 2025 **What do you like best about dbt?** The platform stands out for its exceptional consistency and timeliness. It's intuitive and well-designed, making it easy for our team to get up and running quickly, without the usual complexity or overhead. One of the most notable benefits for us has been its cost-effectiveness. **What do you dislike about dbt?** We've had a fantastic experience working with this platform and are delighted with the results. As our data needs have grown, the platform has adapted seamlessly. **What problems is dbt solving and how is that benefiting you?** This platform has facilitated the implementation of a new data strategy. Our data team now operates more efficiently, without the complexity of coordinating multiple systems or extraction methods. ### 37. User-Friendly Data Modeling with Seamless Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** December 09, 2025 **What do you like best about dbt?** 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 dbt?** In dbt Cloud, I cant work on two different branches at the same time in different browsers. **What problems is dbt 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. ### 38. Pro and cons of DBT at Professional area **Rating:** 5.0/5.0 stars **Reviewed by:** Sai Krishna K. | Infra technology specialist, Enterprise (> 1000 emp.) **Reviewed Date:** July 22, 2025 **What do you like best about dbt?** I worked on DBT for 2 years as a data engineer , It is helpful in writing dynamic SQL queries by creating models and we can write pyspark code. Using both features we can do complex data transformations. Proper lineage is an added advantage **What do you dislike about dbt?** DBT alone is not better , it should be integrated with other warehouses such as Snowflake, where there are other options in market which has both . **What problems is dbt solving and how is that benefiting you?** It helped me in writing complex data transformation queries and cost is also low compared to other platforms available ### 39. ELT made easy with just SQL **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Publishing | Mid-Market (51-1000 emp.) **Reviewed Date:** June 18, 2025 **What do you like best about dbt?** All I need is to write some SQL select statements and specify materialisation type. My table will be created with the type of load I want to perform. Support tickets are resolved quickly from support team. **What do you dislike about dbt?** Sometimes the error is not described aptly. This makes parsing error and debugging difficult. **What problems is dbt solving and how is that benefiting you?** DBT eliminates the need for writing complex code and tedious orchestration jobs. I can create any model with just SQL and orchestration made easy with creating job. Tags feature with lineage made my job easy by running sequence of models. Lineage is helpful in visualising data flow. Lint feature helps with code formatting ### 40. SQL Knowledge enough to become a Data Engineer **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Media Production | Enterprise (> 1000 emp.) **Reviewed Date:** June 18, 2025 **What do you like best about dbt?** Useage of sql. Incremental strategies - which makes append , merge and full refresh easy. Snapshots - which makes building scd type 2 model error free. High Availability - we are using Dbt cloud, i rarely see failure in pipelines. Tests - which helps to validate the data. Configuration is ease. **What do you dislike about dbt?** During development sometimes - changes are cached.So changes will not reflect ,which require removal of target folder or deps or clean to resolve it. **What problems is dbt solving and how is that benefiting you?** Building aggregated tables for Gold layer. Lineage helps in identifying the tables involved incase of new changes. ### 41. A game changer for dashboarding **Rating:** 4.0/5.0 stars **Reviewed by:** Servio Q. | Software Engineer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** October 30, 2024 **What do you like best about dbt?** You would feel really strange at first but after you start the integration will notice how easy to use it is. Great Documentation and community. **What do you dislike about dbt?** The initial deployment is not ideal, and it would be beneficial to have a interface for dbt Core to support analysts who do not use Git on a daily basis but still take advantage of its benefits. **What problems is dbt solving and how is that benefiting you?** Create an UI for some tools like dbt core that would help non tech people give a try to the product ### 42. Best data modeling tool **Rating:** 5.0/5.0 stars **Reviewed by:** Rijul S. | Lead Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** October 15, 2024 **What do you like best about dbt?** beautifull data lineage easy to use and implement dbt learning courses provided by dbt are super usefull data sharing and orchestration is super easy development in cloud ide is very good custoer support is extreme fast and efficient integration with snowflake and GitHub is easy Using daily this tool for building data models **What do you dislike about dbt?** Beta features are very slow releasing. Rest all GREAT **What problems is dbt solving and how is that benefiting you?** building the business models from tables and views in traditional approach is use stored procedure, routines etc. but the new way of building analytics reports and datas marts are using CTE[ common table expression] which dbt solves. dbt also solves and give beautifull lineages, from where your source data is traversrsing to final mart layer. table level lineage is provided by dbt and is super usefull reporting on a single layer is solved by dbt, meaning developer need not to login to data warehouse and dot the development. dbt separates out the data warehouse from modelling layer ### 43. We majorly use DBT cloud for ETL in the organization **Rating:** 4.5/5.0 stars **Reviewed by:** Sushanth U. | Tableau Developer and Admin, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** May 16, 2025 **What do you like best about dbt?** It's simple, SQL based approach and easy to version control. **What do you dislike about dbt?** Challenging to manage large scale and complex dependencies **What problems is dbt solving and how is that benefiting you?** Since it's SQL based, its easy to manage ETL pipeline. ### 44. Elevating Our Data Stack **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2025 **What do you like best about dbt?** dbt's ability to streamline data workflows is excellent **What do you dislike about dbt?** While dbt is great, for extremely large datasets or highly complex transformations, native SQL compilation might still require deep optimization knowledge or workarounds to achieve peak performance **What problems is dbt solving and how is that benefiting you?** Streamlining our data workflows and ELT process ### 45. DBT(Data Build Tool) to build Excellent data models for Quickly and collaboratively **Rating:** 4.0/5.0 stars **Reviewed by:** MAMIDALA V. | Senior System Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** November 19, 2024 **What do you like best about dbt?** The most and best things i like in dbt are it not only helps in creating transformations but can also helps in managing and performing transformations in a view and integrates easily with Big-query i can use and configure the transformation as per the object like table,view or incrementalalong with features like auto-generated lineage graphs and can perform native testing with few lines of codes in a YAML file and can able to re use them. **What do you dislike about dbt?** There is not much about to dislike in dbt the reusable code can be little bit confusing and jumping from one branch to another can be frustrating **What problems is dbt solving and how is that benefiting you?** DBT helped me and my team in testing data pipelines in local in house development and Performing CI/CD pipeline tests along with data migration to targets like Bigquery. ### 46. Overwhelming when it comes to optimize and centralize your big data. **Rating:** 4.5/5.0 stars **Reviewed by:** Muhammad Talha A. | Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** October 10, 2024 **What do you like best about dbt?** 1. The documentation it generates when all the models are designed. It clearly defines which intermediate and final layers are connected to each other. 2. The incremental model runs greatly helped me in optimizing large data models as I was dealing with billions of rows of data. **What do you dislike about dbt?** I did not come across any difficulty in learning DBT as it was pretty basic and I also applied SQL fluff to streamline my coding. As a user, I did not find much difficulty in operating through dbt. **What problems is dbt solving and how is that benefiting you?** Previously, I was using GBQ for creating thousands of lines of stored procedures and so many tables were interconnected inside of it. It was pretty difficult to determine which tables are made up of what. When I started using DBT, I was able to quickly determine and find the staging and intermediate layers for the purpose of creating a final layer and the documentation it creates was awesome. I am talking about dbt docs generate and dbt docs serve. ### 47. DBT review **Rating:** 3.5/5.0 stars **Reviewed by:** Sangavi D. | Data Engineering, Mid-Market (51-1000 emp.) **Reviewed Date:** June 24, 2025 **What do you like best about dbt?** Using SQL queries, it is easy platform to transform the data. **What do you dislike about dbt?** For legacy system its is not useful for tranformation **What problems is dbt solving and how is that benefiting you?** Data transformation ### 48. DBT review **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** June 24, 2025 **What do you like best about dbt?** DBT is user friendly and helps keep our data reliable, updated and secure **What do you dislike about dbt?** Upgrading from core is really expensive without there being any significant differences besides number of users **What problems is dbt solving and how is that benefiting you?** DBT helps us build our models and keep them refreshed in our warehouse ### 49. Great experience with dbt **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** October 01, 2024 **What do you like best about dbt?** dbt is the best Transformation tool out there in the industry and I love dbt for its testing capabilities and modeling and semantic layer. Ease of use and how easily you could maintain It is easy to integrate with other tools like integration. **What do you dislike about dbt?** dbt should add more AI apacilities faster **What problems is dbt solving and how is that benefiting you?** dbt is solving all the data integration, integrity and data quality problems for our company while serving as a great transfomration tool ### 50. It's like seeing an old friend that you really liked but haven't seen for a while. **Rating:** 5.0/5.0 stars **Reviewed by:** 📈 Rho L. | Business Intelligence Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** January 18, 2024 **What do you like best about dbt?** At it's core, DBT aligns three technologies to deliver knowledge better: SQL, YAML, & Jinja. You can do a lot with just SQL and YAML. Adding in Jinja makes SQL feel a lot more like traditional development. I kinda missed that. It's like seeing an old friend that you really liked but haven't seen for a while. dbt is magic for transforming and modeling data. It's a platform that allows us to wrangle, shape, and organize the data to model the business. With the help of DBT, we can implement the principle of separation of concerns to organize and manage our transformations. One of the key tools DBT offers is Directed Acyclic Graphs (DAGs), maps that illustrate the path our data takes from source to the final destination. These maps illustrate the data transformation arc. We start with the source data, which is often messy and unrefined. We use DBT to perform a series of transformations, taking the data on a journey from a multiverse of chaos to a world of understanding. We clean the data, apply business rules, and ensure the data conforms to our business dimensional models. These models or core business logic serve as the foundation for reporting. As we progress along the transformation arc, our data starts to take shape. We can build data marts for specific business areas or functions. These data marts are built with our business dimensional models, ensuring that the data is structured in a way that supports efficient analysis and reporting. Reporting on top of our business dimensional models. With the data now organized and modeled in a meaningful way, we can unlock valuable insights and empower decision-makers with actionable information . . . at scale. We can slice and dice the data, apply filters, and drill down into specific dimensions to understand trends, patterns, and outliers. The reports we develop are consistent because they come from a single source of truth, the business dimensional model. **What do you dislike about dbt?** dbt requires a mindset change. You have to buy into how they think about modeling. It's opinionated. dbt is method-agnostic (data vallt, mesh, kimball). But structure matters and you need to spend some time to understand dbt's mindset around stricture. **What problems is dbt solving and how is that benefiting you?** Let me tell you about the state of our data. At the time, we didn’t know. That was the issue. It was a black box. Our data model was opaque with logic scattered all across the data stack. As we pick around the edges a picture starts to form. Imagine a dense, thorny briar patch, each thicket representing a tangled mess of information. That's how I see it—unruly, interlacing, and chaotic. Management has a different take. They call it "spaghetti," a swirling plate of tangled noodles. It’s actually not far from the truth. Each report fed directly from the source, the logic for each was self-contained and sometimes borrowed. ## dbt Discussions - [What is DBT data Modelling?](https://www.g2.com/discussions/what-is-dbt-data-modelling) - 2 comments - [What is DBT technology?](https://www.g2.com/discussions/what-is-dbt-technology) - 2 comments - [What is DBT database tool?](https://www.g2.com/discussions/what-is-dbt-database-tool) - 1 comment - [What is DBT tool used for?](https://www.g2.com/discussions/what-is-dbt-tool-used-for) - 2 comments - [View dbt pricing details and edition comparison](https://www.g2.com/products/dbt/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-14+19%3A11%3A51+-0500&secure%5Bsession_id%5D=c6014ace-7705-4d17-8bf6-5df9fde1ee43&secure%5Btoken%5D=35450b88cabaa5edb7d38596b6a9fd4a6741aff2d3151c50dbc190d30a4dae79&format=llm_user) ## dbt Integrations - [Amazon EC2](https://www.g2.com/products/amazon-ec2/reviews) - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) ## dbt Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards **Data Source Access** - Breadth of Data Sources - Ease of Data Connectivity - API Connectivity **Management** - Auditing **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Automation** - Workflow Automation - Multi-platform support - Data Management **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Deployment & Integration - Semantic Layer Tools** - Multi-Environment & Multi-Cloud Support - Open API & SDK Integration **Data Transformation** - Real-Time Analytics - Data Querying **Data Interaction** - Profiling and Classification - Metadata Management - Data Modeling - Data Joining - Data Blending - Data Quality and Cleansing - Data Sharing - Data Governance **Functionality** - Transformation - Automation - Scalability **Analytics** - Analytics capabilities - Dasboard visualizations **Functionality** - Documentation management - Platform support - Template functionality **Data Connectivity & Federation - Semantic Layer Tools** - Cross-Source Query Federation - Dynamic Schema & Metadata Adaptation **Data Exporting** - Breadth of Integrations - Ease of Integrations - Data Workflows **Monitoring and Management** - Data Observability - Testing capabilities **Administration** - Error Alerts - Service Automation - Workflow management **Data Modeling & Metrics - Semantic Layer Tools** - Derived & Calculated Metrics - Time Intelligence Functions **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Functionality** - Identification - Correction - Normalization - Preventative Cleaning - Data Matching **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation **Agentic AI - Data Warehouse Automation** - Proactive Assistance **Performance Optimization - Semantic Layer Tools** - Query Caching & Acceleration - Adaptive Query Optimization **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Operations** - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Management** - Reporting - Automation - Quality Audits - Dashboard - Governance **Generative AI** - AI Text Generation - AI Text Summarization **Governance - Semantic Layer Tools** - AI Governance & Observability - Metric Lineage for AI Training Data - Version Control & Change Impact Analysis **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Generative AI** - AI Text Generation - AI Text Summarization **Advanced Intelligence - Semantic Layer Tools** - Natural Language Query Interface - Semantic Layer for AI/ML Models - Recommendation Engine **Agentic AI Enablement - Semantic Layer Tools** - Agentic Query Orchestration - Contextual Reasoning Layer - Workflow Automation via Semantic Agents **Building Reports** - Data Transformation - Data Modeling - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top dbt Alternatives - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (740 reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 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