Datafold

Datafold is a unified platform for proactive data quality, specifically designed to empower data engineering teams. It addresses the most critical and challenging aspects of modern data workflows: ensuring absolute data integrity and streamlining the modernization of data infrastructure. By harnessing the power of AI, advanced Large Language Models (LLMs), and its proprietary "data diffing" technology, Datafold automates the most error-prone and time-consuming tasks. This allows teams to build highly reliable data products at a much faster pace.

The platform is founded on the principle that data quality should be a proactive, integral part of the development lifecycle, not a reactive afterthought. It provides the tools necessary for companies to move beyond the limitations of legacy systems and confidently build an AI-ready data stack with unparalleled speed and accuracy.

How to use Datafold

Datafold integrates seamlessly into existing data engineering workflows, providing a structured and automated approach for various tasks.

For Data Migrations:

1. Plan: Leverage detailed column-level lineage to map all data dependencies and accurately assess the complexity of the migration. This creates a comprehensive blueprint, making project timelines predictable and transparent.
2. Translate: The AI-driven Datafold Migration Agent (DMA) automatically converts any SQL dialect or GUI-based transformation logic into the target system's syntax (e.g., migrating from Oracle PL/SQL to Snowflake SQL). It employs an intelligent feedback loop to iteratively refine the code until perfect functional parity is achieved.
3. Validate: This is where Datafold's core "data diffing" capability excels. It performs a value-level comparison of every record between the legacy and new systems, automatically verifying 100% data accuracy without the need for manual sampling or tedious scripting.
4. Ship: Upon successful validation, Datafold generates comprehensive reports and auditable data diff evidence. This provides concrete proof of data parity, which accelerates stakeholder sign-off and allows for the confident decommissioning of the legacy system.

For Data Quality Testing in CI/CD:

1. Integration: Connect Datafold to your version control system, such as GitHub or GitLab.
2. Automated Testing: When a developer opens a pull request containing changes to data transformation code (e.g., a dbt model), Datafold is automatically triggered to run a data diff between the development and production environments.
3. Review and Deploy: The results are posted as a clear, concise comment within the pull request. This allows reviewers to see the exact impact of the code changes on the data at a value level, preventing data quality issues from ever reaching production.

Core Features of Datafold

• AI-Powered Data Migration (Datafold Migration Agent - DMA): Automates the entire migration lifecycle, from SQL code translation across disparate dialects to complete end-to-end validation. It intelligently handles complex edge cases, such as differences in data type handling, non-deterministic functions, and character encoding, to deliver up to a 6x faster migration.
• Data Diffing: A powerful validation engine that performs efficient, value-level comparisons of entire datasets, even those with billions of rows. It precisely identifies any additions, deletions, or modifications to guarantee 100% data parity.
• Proactive CI/CD Testing: Integrates directly into the development workflow (shift-left testing) to test data transformation code before deployment. It includes impact analysis to visualize how changes affect downstream tables, BI dashboards, and reverse ETL pipelines.
• Data Monitoring & Observability: Provides ML-powered anomaly detection to monitor data health in production. Users can define monitors as code (YAML) or via the UI for metrics, schema changes, and scheduled cross-database diffs, with real-time alerts through Slack, PagerDuty, and email.
• Column-Level Lineage: Delivers a comprehensive map of data dependencies that extends beyond the data warehouse to BI tools (Tableau, Looker, Power BI) and other applications. This is crucial for impact analysis, root cause analysis, and compliance.
• Data Replication Testing: Continuously validates data between source and target systems in ongoing replication pipelines, ensuring that mission-critical data remains synchronized and accurate at all times.

Use Cases for Datafold

• Data Stack Modernization: Drastically accelerate migrations from legacy systems (e.g., Oracle, Teradata, SQL Server) to modern cloud data platforms (e.g., Snowflake, BigQuery, Databricks). For instance, Faire migrated over 5,000 tables from Redshift to Snowflake six months ahead of schedule using Datafold.
• dbt Development & Testing: Supercharge dbt workflows by automatically testing every pull request, guaranteeing that changes to dbt models do not introduce data quality regressions.
• Ensuring BI Dashboard Accuracy: Use column-level lineage to trace data from its source all the way to BI dashboards, ensuring that business reports are built on a foundation of reliable and accurate data.
• Validating Replication Pipelines: For organizations using data ingestion tools like Fivetran or Airbyte, Datafold can schedule regular data diffs to certify that the data in the destination warehouse perfectly mirrors the source.

Advantages of Datafold

• Unprecedented Speed: Compresses migration timelines from years to weeks by automating the most labor-intensive parts of the process.
• Guaranteed Accuracy: Moves beyond simple row counts to exhaustive, value-level validation, eliminating the risk of data loss or corruption.
• Increased Developer Velocity: Catches data bugs early in the CI/CD pipeline, empowering engineers to ship code faster and with greater confidence.
• Proactive, Not Reactive: Implements a "shift-left" philosophy for data quality, preventing issues before they can impact production systems and business operations.
• Enhanced Trust & Collaboration: Provides auditable, undeniable proof of data quality, which builds trust with business stakeholders and streamlines project approvals.
• Secure & Flexible Deployment: Offers multiple deployment models (SaaS, single-tenant VPC, self-hosted) and is compliant with major standards like SOC2 Type II, GDPR, and HIPAA.

Pricing and Plans

Datafold provides customized pricing tailored to the unique requirements of each team. The pricing model is primarily based on the number of users and the volume of tables being monitored and tested. While the platform is typically sold as a comprehensive solution, specific features, such as one-time migration conversion and validation or standalone column-level lineage, can be purchased separately. To obtain an accurate price quote, prospective customers should contact the Datafold sales team by requesting a demo on their official website.