
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
Details
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Pricing
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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All fees are non-cancellable and non-refundable except as required by law.
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Right one for testing
Review for Datafold
Helps in data managing of huge chunks of table, rows and records of data in day to day usage.
Data accuracy and quality kpi achievement.easily integrates with modern data stacks such as Amazon redshift, snowflake,gitlab and GitHub
Great platform for improving data quality
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.