A data quality platform that scales with your team
and data

Datafold offers multiple deployment options to meet your organization’s security needs. Deployment options include a multi-tenant/SaaS, dedicated cloud (customer-hosted), and a dedicated cloud (Datafold-hosted) option.

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.

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.

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.

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.

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.

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.