A data quality platform that scales with your team and data
Datafold gives teams the tools to manage data quality at scale while prioritizing security, compliance, and fast performance.
Optimized for enterprise security and scale
Standardized and proactive data quality testing for any workflow
Migration conversion & validation
Shorten your data migrations by more than 6 months with Datafold’s AI-powered SQL conversion and cross-database data diffing.
CI/CD deployment testing
Catch and fix data quality issues before they hit production with automated CI/CD testing. Govern the code review process and deploy with greater speed and confidence.
Data monitoring & observability
Stay focused on what’s important with automated anomaly detection that cuts through the noise. Quickly resolve any data quality issues with detailed root cause analysis.
.avif)
FAQ
What type of deployment options does Datafold offer?
Datafold offers multiple deployment options to meet your organization’s security needs. Deployment options include a multi-tenant/SaaS, dedicated cloud (customer-hosted), dedicated cloud (Datafold-hosted), and on-premise option.
How does Datafold help my team improve our data quality?
Datafold is the unified platform for proactive data quality, enabling teams to improve data quality across all workflows. Specifically, Datafold supports powerful tooling for automated data testing, lineage, data monitoring, and data reconciliation. Here’s an overview:
- Automated deployment testing: Prevent data quality issues from entering production pipelines with automated data testing during the CI/CD process. Ensure all code changes undergo consistent data testing standards, and streamline the deployment process.
- Data monitoring and observability: Continuous visibility into your data pipelines with Data Diffs, Data Tests, Metrics Monitoring, and Schema Change Alerts helps catch and resolve issues early. Integrate with Slack, Email, and Webhooks to be notified immediately of data anomalies or test failures.
- Column-level lineage and impact analysis: Understand the effects of data transformation code changes before deployment to prevent quality issues, and develop a birds eye view on how your data moves from source to downstream uses.
- Data reconciliation: Perform value-level cross-databases comparisons at scale to ensure successful data replication and migrations.
What data does Datafold store?
Datafold acts as and needs similar permissions to Business Intelligence (BI) tools: Read (SELECT) access to all relevant data and write access to only a dedicated temporary schema (for caching results). Datafold caches some high-level aggregates and samples in its internal database to provide a quick user experience.
How can I limit who has access to data in Datafold?
In Datafold, Role-Based Access Control (RBAC) works seamlessly with Single Sign-On (SSO) from your data warehouse to provide a secure and efficient authentication and access control mechanism for users.
How does Datafold’s pricing work?
Datafold is a data quality platform that is usually priced based on the number of users and tables being monitored and tested. It is generally purchased by data teams as a comprehensive platform for multiple testing purposes. However, for teams interested in specific features such as one-time migration conversion and validation or column-level lineage, these can be purchased separately.
How does Datafold test and monitor data at scale?
Datafold customers regularly test and monitor extremely large datasets. Data teams can leverage Datafold’s efficient checksumming algorithm, filtering, sampling, tagging, REST API, monitors-as-code, and more to manage data tests and monitors at scale.
Does Datafold integrate with my data warehouse?
Datafold integrates with most major SQL and NoSQL warehouses, including Snowflake, Databricks, Google BigQuery, MongoDB, and more. For the full list of database options, please see our Integrations page.
