Sold by: DatafoldÂ
Automated testing for Data Engineers. Datafold is the fastest way to validate dbt model changes during development, deployment & migrations. With Datafold, data engineers can audit their work in minutes without writing tests or custom queries. Integrated into CI, Datafold enables data teams to deploy with full confidence, ship faster, and leave tedious QA and firefighting behind.
4.5
Overview
Datafold is the fastest way to validate dbt model changes during development, deployment & migrations. With Datafold, data engineers can audit their work in minutes without writing tests or custom queries. Integrated into CI, Datafold enables data teams to deploy with full confidence, ship faster, and leave tedious QA and firefighting behind.
Automate proactive testing for all data transformations. Supercharge your dbt workflows with seamless dbt Cloud and Core integrations.
Know exactly what will happen to data and data applications once the code is deployed, right in the pull request. Identify breaking changes, sudden metric shifts and edge cases before they do any damage to the business.
Stop guessing what this regex does or arguing if that CASE WHEN statement has correct logic. No more custom scripts and audit spreadsheets to fill.
Stop surprising your data users with unexpected metric changes and broken dashboards. Easily share impact reports with everyone and give heads up before deploying the changes to production.
Manual data testing is hard, tedious, and error-prone. Focus on what matters and not on writing boilerplate tests, custom scripts and filling out audit spreadsheets.
With full visibility into every change, everyone, not just data team, can contribute, because testing and reviewing code is so easy!
Highlights
- Development testing for dbt
- Deployment testing for dbt
- Migration testing
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Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
10 Developers | 10 Provisioned Developers | $30,000.00 | |
5 Developers | 5 Provisioned Developers | $15,000.00 | - |
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Sentiment is AI generated from actual customer reviews on AWS and G2
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AI generated from product descriptions
Data Transformation Validation
Automated testing and validation of data transformations using advanced comparison techniques
CI/CD Integration
Seamless integration with continuous integration and deployment workflows for data models
Change Impact Analysis
Comprehensive detection and reporting of breaking changes, metric shifts, and potential edge cases in data models
dbt Compatibility
Native support and integration with dbt Cloud and dbt Core platforms for data transformation workflows
Automated Code Review
Automated code analysis and testing without requiring manual test script or custom query development
AI-Powered Development Environment
Integrated code IDE with native AI capabilities for data engineering and analytics development
Pipeline Orchestration
State-aware scheduling system for dbt and Python data pipelines with advanced monitoring capabilities
Data Lineage Tracking
Column-level lineage context and impact analysis for comprehensive data transformation workflows
Multi-Platform Integration
Supports seamless integration with data ingestion and visualization tools like Fivetran, Tableau, and PowerBI
Performance Optimization
AI-driven agents for continuous warehouse cost monitoring and operational efficiency
Data Pipeline Transformation
Open source SQL-based data transformation tool for building and orchestrating data pipelines
Integrated Development Environment
SQL Runner with Jinja support and version control workflow guidance
Job Scheduling
Custom scheduling capabilities for production jobs with incremental testing and deployment options
Enterprise Security
SOC2 Type II compliance with single sign-on and role-based access control mechanisms
Deployment Orchestration
Professional-grade environment for stable and reliable dbt project deployment and management
Standard contract
No
Customer reviews
reviewer2784462
Automated data quality checks have reduced errors and saved significant validation time
Reviewed on Dec 04, 2025
Review from a verified AWS customer
What is our primary use case?
I have been using Datafold for one year. I use Datafold in a consulting context to validate changes in the data model and the DI pipeline. I used Datafold in one project where our team safely refactored key revenue reports built on top of our customer data warehouse. Before merging any DBT or SQL changes, Datafold automatically compared the old and new version of the main fact table. I used Datafold on the customer side, and they are using Datafold on cloud data.
What is most valuable?
Datafold offers many features, but the best one for me is that it extracts you from data difficulties, data quality, and observability challenges. It is a data quality and observability platform, and we use Datafold for that purpose.
The data quality feature is helpful in understanding missing values or identifying issues in the data, which stands out to me about those capabilities. A lot of positive impact has come from Datafold in my organization because we needed a data observability tool to observe our data flows and ensure good data quality in our data warehouse and customer data.
It reduced many errors and also reduced manual impact by automating a lot of work.
What needs improvement?
Datafold can be improved mainly on usability, scale, broader integration, and pricing flexibility.
For how long have I used the solution?
I have been working in my current field since 2014.
What do I think about the stability of the solution?
Datafold is stable in my experience.
What do I think about the scalability of the solution?
I think Datafold has good scalability, though I am not completely certain about its full scalability capabilities.
How are customer service and support?
I have not yet interacted with their support team, but may do so in the future.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We previously used a custom solution, but it was not suitable for the customer, which is why we switched to Datafold.
What was our ROI?
We have seen a return on investment from using Datafold with a 30% time saved.
What other advice do I have?
I advise using Datafold to ensure good data quality and observability, and to integrate Datafold into the CI/CD pipeline. We use AWSÂ , for example, as our cloud provider. I rate this product a 9 out of 10.
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?
Amazon Web Services (AWS)
Information Technology and Services
Right one for testing
Reviewed on Apr 20, 2023
Review provided by G2
What do you like best about the product?
Awesome workflow, which is really a great feature of Datafold. Traditional way of doing data transfers is laborious. But this one helps to the best of capabilities. My fellow team was impressed.
What do you dislike about the product?
Nothing as such to say about negative here. But breadth of usage can be enhanced. This is not a drawback for sure. May be in coming days, will explore more and revert. For now all good
What problems is the product solving and how is that benefiting you?
Integration of data and managing data quality are two things that pose a big challenge in front of us. But Datafold made it easier to look and saved our time and effort
Telecommunications
Review for Datafold
Reviewed on Apr 19, 2023
Review provided by G2
What do you like best about the product?
Makes life easier with SQL code reviews helping find the hidden changes we made we dint know to our data.It gives ability to quality check the things in our own way with very much lesser errors compared to manual testing.
What do you dislike about the product?
Nothing in particular, a brief guide with documentation would have been justified.
Helps in data managing of huge chunks of table, rows and records of data in day to day usage.
Helps in data managing of huge chunks of table, rows and records of data in day to day usage.
What problems is the product solving and how is that benefiting you?
Makes life way easier for automated testing
Data accuracy and quality kpi achievement.easily integrates with modern data stacks such as Amazon redshift, snowflake,gitlab and GitHub
Data accuracy and quality kpi achievement.easily integrates with modern data stacks such as Amazon redshift, snowflake,gitlab and GitHub
Sanjay D.
Great platform for improving data quality
Reviewed on Apr 19, 2023
Review provided by G2
What do you like best about the product?
Datafold is a valuable data testing and monitoring tool that helps users ensure the quality and accuracy of their pipelines. It detects errors, improves data quality, and helps troubleshoot problems quickly, increasing confidence in the data.
What do you dislike about the product?
Datafold is an amazing platform I like all the features provided by this, but there is one limitation I observed that it has a steep learning curve, limited integration options, and a commercial subscription requirement these factors challenges for us in adopting and using this tool effectively.
What problems is the product solving and how is that benefiting you?
Datafold offers a complete picture of data pipeline health, performance, and quality for data teams to monitor and ensure smooth operations.
Datafold automates the process of detecting data pipeline issues and ensures data quality. It saves time and resources by automating data testing, which would otherwise be time-consuming and error-prone if done manually.
Datafold automates the process of detecting data pipeline issues and ensures data quality. It saves time and resources by automating data testing, which would otherwise be time-consuming and error-prone if done manually.
Madhusudhan M.
"Best tool for data quality and consistency"
Reviewed on Apr 18, 2023
Review provided by G2
What do you like best about the product?
It is simple and User friendly UI. It provides real-time updates.It includes best range in build tests and checks for data quality, consistency and saving time.
What do you dislike about the product?
There is no negative thing about the software but the price is little bit high compared to other softwares in the market.
What problems is the product solving and how is that benefiting you?
It is very much useful for data engineers for automation testing.Data Quality monitoring is too easy without any hard failures compared to other tools which are inflexible and takes hard failures.