Sold by: dbt Labs
Deployed on AWS
dbt Platform is a hosted service that helps data analysts and engineers product-ionize dbt deployments. It comes equipped with turnkey support for scheduling jobs, CI/CD, serving documentation, monitoring & alerting, and an Integrated Developer Environment (IDE).
4.7
Overview
dbt is an open source data transformation tool that allows anyone comfortable with SQL to author their own data pipelines. dbt Platform provides a stable, professional grade environment for building and orchestrating dbt projects. It's the fastest and most reliable way to deploy dbt.
Find out if dbt Platform works with your data base or data warehouse here: https://docs.getdbt.com/docs/available-adapters
Sign up for a free, 14-day trial here: https://www.getdbt.com/signup/
Highlights
- Save time with an IDE built for dbt, including a SQL Runner capable of running Jinja; and guided process to enforce version control best practices, even for users new to git
- Set up custom schedules to run production jobs including incremental testing upon change or before deployment.
- dbt Platform provides professional grade support and security for the enterprise (SOC2, Type II compliance, SSO, role-based access)
Details
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Deployed on AWS
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Dimension | Description | Cost/12 months |
|---|---|---|
dbt Platform | dbt Platform Plan: 10 Develop Licenses | $48,000.00 |
Dimension | Cost/unit |
|---|---|
Additional overages as defined in contract | $0.01 |
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Support
Vendor support
Complete documentation is available at https://docs.getdbt.com/docs/dbt-cloud/cloud-overview/ . support@getdbt.com
AWS infrastructure support
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Integrated Development Environment
IDE built for dbt with SQL Runner capable of executing Jinja templates and guided version control enforcement for git best practices
Job Scheduling and Orchestration
Custom scheduling capabilities for production jobs with incremental testing triggered on change or before deployment
CI/CD Pipeline Support
Continuous integration and continuous deployment functionality for automated dbt project workflows
Enterprise Security and Compliance
SOC2 Type II compliance certification, single sign-on (SSO) authentication, and role-based access control
Monitoring and Alerting
Built-in monitoring and alerting capabilities for tracking job execution and system health
AI-Native Code Development Environment
Integrated development environment with AI capabilities for coding data pipelines using dbt and Python, featuring built-in warehouse access and column-level lineage context.
State-Aware Pipeline Scheduling
Scheduler supporting state-aware execution of dbt and Python data pipelines with column-level impact analysis for CI testing.
Data Lineage and Impact Analysis
Column-level lineage tracking and impact analysis capabilities for understanding data dependencies and transformation effects across pipelines.
Warehouse Cost Optimization
AI-agent based monitoring and optimization system operating continuously to reduce warehouse operational costs.
Data Pipeline Orchestration Integration
Support for orchestrating multi-tool data workflows including Fivetran ingestion, data transformation pipelines, and downstream application refreshes for Tableau and PowerBI.
Environment Automation
Manages Snowflake infrastructure as configuration and code with deployment and lifecycle management capabilities for data applications and products.
Pipeline Orchestration
Builds end-to-end data pipelines and orchestrates data ingestion, modeling, and testing tools with integrated pipeline construction capabilities.
Continuous Integration/Continuous Deployment
Provides automated CI/CD workflows for building, testing, and deploying data products and applications on Snowflake.
Unified Observability
Collects, unifies, manages, and shares operational metadata to provide comprehensive visibility across data products and infrastructure.
Data Product Lifecycle Management
Delivers full lifecycle management for data products including development, testing, debugging, validation, and execution of Snowflake workloads.
Standard contract
Customer reviews
Syed A.
A Developer Friendly Transformation Tool
Reviewed on Feb 03, 2026
Review provided by G2
What do you like best about the product?
I like best about dbt is how it brings a clean, developer‑friendly structure to analytics work. It makes modeling and transforming data feel organized and predictable, thanks to its simple SQL‑first approach and clear project layout. I also really appreciate how dbt encourages good engineering practices such as version control, testing, documentation. So the entire workflow becomes more reliable and collaborative.
What do you dislike about the product?
I dislike about dbt is that some parts of the workflow can feel a bit inflexible, especially when you're trying to customize how tests or models behave in more complex projects. It also relies heavily on command‑line and configuration files, which can become demanding as the project grows. On top of that, dbt doesn’t handle ingestion or real‑time needs, so user often need additional tools to complete the pipeline, which makes the setup feel less seamless.
What problems is the product solving and how is that benefiting you?
dbt solves the problem of scattered, inconsistent transformation logic by giving user a clean, structured way to manage SQL models, tests, and documentation in one place. I no longer needs to deal with random queries or unclear business rules, everything becomes version‑controlled and easy to trace. Which helps me in my workflows to become productive.
Scott J.
Reliable transformation practices at scale
Reviewed on Jan 27, 2026
Review provided by G2
What do you like best about the product?
One thing that I find impressive about dbt is that it promotes discipline in writing of transformations. It transformed my approach towards the way I deal with my work, as I now think twice before imposing changes. I use it on a regular basis, and it has enhanced teamwork since logic has less difficulty in reviewing and discussion. This has saved time on quick fixes and has assisted us in developing more confidence on outputs that may be shared.
What do you dislike about the product?
What I do not like about dbt is that there is a huge effect of little errors in the models. Some of them may break down under the pressure of having a few downstream pieces broken when there is a slight change. It is time consuming and can even bring several individuals into the same problem when it comes to debugging those chains. In my case, this retards progress and results in context switching which can be annoying when time lines are near.
What problems is the product solving and how is that benefiting you?
Dbt eliminates the issue of vague ownership and reasoning. It provides organization where responsibilities are clearly seen which enhances cooperation. In my case, it implies a reduced number of handoff problems and a streamlined collaboration. Co-workers become bolder in changes and tasks are less responsive on a daily basis. It has simplified our working process and made it more predictable in general.
Shubham-Agarwal
Incremental data models have cut full refresh time and support trusted executive reporting
Reviewed on Jan 22, 2026
Review from a verified AWS customer
What is our primary use case?
I am currently working with dbt and Snowflake together. We use dbt for data transformation purposes. We obtain the data and store the raw data directly into Snowflake , then perform all transformations using dbt to prepare the data for reporting purposes.
We use dbt's modular SQL models. In dbt, we do not use full refresh or full data refresh. We have incremental strategies in place that only compute or transform incremental data, which operates in a CDC architecture. This approach is very fast, and we use it on a daily basis. We have scheduled all our dbt models using Airflow to run according to the scheduled time.
We use dbt's testing framework and the inbuilt functionality of dbt testing. For example, we use dbt's built-in tests to identify not null values and determine how many not null columns and values exist in each column. Beyond the built-in functionality, we have written custom SQL scripts to create external test cases on our models.
We ensure that incorrect or incomplete data does not go into the reporting layer because it can impact the business. We always perform dbt tests on our landing or raw data to ensure the correctness and completeness of the data before loading it into the final reporting layer. These reports are used by higher management, so we ensure that incorrect data is not published into the reporting layer for the Power BI reports.
We use dbt's documentation site generator. In dbt, we have YML file functionality, which can be used for creating documentation for each model. Whenever we make modifications to a model, we always update the YML file so we can track the history of how frequently we change the model. When new team members join, they can refer to this documentation to understand the data lineage and the data transformation strategy of the project.
What is most valuable?
dbt is very fast compared to the traditional tools. Previously, I worked on SSIS , which is provided by Microsoft, and data transformation took a considerable amount of time when dealing with large amounts of data. Since dbt works on the ELT architecture rather than the ETL architecture, it is much faster than traditional data transformation tools.
Previously, we were using SSIS packages, which were very slow. Recently we migrated all our SSIS packages to dbt models. After the migration, we moved the data from SQL Server to Snowflake. Previously, our data pipeline took around two days to load complete data when performing a full refresh. Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
What needs improvement?
I am not very familiar with dbt's version control system.
I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three years, so I am exploring more functionalities and cannot see any limitations or improvement areas at this time.
In the past, I used the seed functionality, which is used to load raw files, individual files, or static files into the database. Going forward, if dbt can improve or implement more features on the seed side, that would be beneficial, especially when we have large files available that take time to load the data into Snowflake database.
For how long have I used the solution?
I have been working with dbt for the last three years.
What do I think about the stability of the solution?
I have not experienced any crashes, performance issues, or anything regarding stability and reliability.
What do I think about the scalability of the solution?
I find dbt very scalable.
How are customer service and support?
The dbt support team is very responsive. Whenever we have any issues on the dbt side, we always reach out to them. We did not face any challenges in the initial setup. I would rate the technical support a nine out of ten.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Previously, we were using SSIS packages, which were very slow. Recently we migrated all our SSIS packages to dbt models. After the migration, we moved the data from SQL Server to Snowflake. Previously, our data pipeline took around two days to load complete data when performing a full refresh. Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
The main reason we decided to switch to dbt is performance. As mentioned earlier, every quarter we perform a full refresh, and that refresh took considerable time on SQL Server. Since we had to migrate because our data is very large and growing daily, we adopted dbt because Snowflake is very fast. In Snowflake, the storage layer and the computation layer are separate, which is not present in the SQL Server traditional database. That is why we moved from SQL Server to Snowflake and from SSIS to dbt.
How was the initial setup?
We evaluated Databricks as well, but ultimately the client wanted to adopt Snowflake and dbt technologies only.
What about the implementation team?
We took help from Snowflake directly, the Snowflake company, for the Snowflake side. The dbt side is maintained or set up by our infrastructure team.
What was our ROI?
Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh. The client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data.
What's my experience with pricing, setup cost, and licensing?
The pricing, setup cost, and licensing cost are managed by our infrastructure teams. As data engineers, we are not familiar with these details.
I need to check with my infrastructure team on whether we purchased dbt through the AWS Marketplace or directly from the local vendor.
Which other solutions did I evaluate?
Since dbt has a license cost, if a company is small and does not have much budget, they can explore other tools because there are other tools that provide the same functionality at a lower cost. If an organization is small, they can explore other products as well.
What other advice do I have?
I am currently working with Power BI, Tableau, Python, Databricks , Snowflake, and PySpark in the current project. I would rate my overall experience with dbt a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Joseph S.
Speedy but however it is quite pricey and resource hungry
Reviewed on Jan 20, 2026
Review provided by G2
What do you like best about the product?
The way it handles large amounts of data, as well as how it integrates into AWS (S3/Glue) is great. This allows me to avoid building custom pipelines which would have been very time consuming and caused additional headaches and due to its columnar database design, all of my complex query requests are processed in a timely manner which means I do not fall asleep while waiting for results.
What do you dislike about the product?
Vacuuming Tables… seriously, I have to manually vacuum and analyze tables to keep this thing running smoothly? It looks like 2005. Managing the clusters and nodes is also a pain – it’s not true serverless. If you’re not paying close attention to the costs, they will jump up way too high.
What problems is the product solving and how is that benefiting you?
This has allowed us to move away from a pandas-based reporting solution, which crashed consistently. We now can process billions of records from our retail business and have a working dashboard. Its architecture provides separate storage and compute, allowing us to scale our compute resources as much as needed based on the demand for reports by management. Most importantly, it has reduced the amount of yelling from our data team regarding slow query performance.
Duvan Dario D.
Good performance in BigQuery, but the focus on monetization disappoints
Reviewed on Jan 06, 2026
Review provided by G2
What do you like best about the product?
dbt core for data transformations on BigQuery
What do you dislike about the product?
The change in the business model to focus on monetization has been notable.
What problems is the product solving and how is that benefiting you?
Generate data in a structured manner and with version control in ETLs is easy using SQL. This tool facilitates the handling and organization of information during the extraction, transformation, and loading processes.