Datafold
Data reliability platform that prevents bad data from reaching production through automated testing and monitoring.
Datafold is a data reliability platform that helps data teams prevent bad data from reaching production. Their core innovation is "data diff" - the ability to compare datasets at scale to identify changes and anomalies. This makes it particularly powerful for CI/CD workflows and change management in data pipelines.
The platform focuses on proactive data quality management, integrating directly into development workflows to catch issues before they impact downstream systems. Datafold is especially strong for teams that want to apply software engineering best practices to their data operations.
Patreon BI
Creator Economy
"Datafold helps us prevent report breaks and maintain data reliability across our business intelligence infrastructure."
Source: datafold.com
Data Diff
Compare datasets at scale to identify changes and anomalies
Column-level Lineage
Detailed tracking of data transformations and dependencies
CI/CD Integration
Automated testing in pull requests and deployment pipelines
Impact Analysis
Understand downstream effects of data changes
- •Excellent data diffing capabilities
- •Strong CI/CD integration for data teams
- •Fast setup and time to value
- •Detailed column-level lineage
- •Developer-friendly approach
- •Limited to SQL-based databases and warehouses
- •Higher pricing point for smaller teams
- •Focused primarily on structured data
- •Less comprehensive than full observability platforms
Professional
From $2,000/month
Pricing based on data volume and number of connections
- • Data diff and monitoring
- • Column-level lineage
- • CI/CD integrations
- • Standard support
Enterprise
Contact Sales
Advanced features and enterprise-grade security
- • All Professional features
- • Advanced security controls
- • Priority support
- • Custom integrations
Analytics Engineers
Teams using dbt and modern data transformation tools
Data Engineers
Teams wanting CI/CD for data pipelines
SQL-Heavy Workflows
Organizations primarily using SQL data warehouses
Time to Value
1 week for data diffing, 2-3 weeks full setup
Technical Requirements
SQL warehouse, Git integration, dbt (optional)
Implementation Complexity
Moderate - requires CI/CD integration
Required Expertise
Analytics engineers, DevOps knowledge
Onboarding Support
Hands-on implementation support included
Learning Curve
Moderate - familiar to software engineers
Native Integrations
Snowflake, BigQuery, Redshift, dbt, GitHub, GitLab
API Quality
REST API, Python SDK, CLI tools
Data Export/Import
CSV exports, API access, diff reports
Webhook Support
Slack, email, custom webhooks for CI/CD
Pre-built Connectors
15+ SQL warehouse connectors
Custom Development
Moderate - custom rules and metrics
Implementation Cost
$10K-25K including setup and training
Ongoing Maintenance
Low-moderate - some rule maintenance needed
Contract Terms
Annual contracts, usage-based pricing
ROI Timeline
1-3 months via prevented data issues
vs Alternatives
Mid-range pricing, strong for dbt users