Recce

Product Introduction

1. Recce is a data validation platform designed to help data teams analyze the impact of changes in dbt (data build tool) pipelines before deploying to production. It automatically generates actionable checklists for pull request reviews by contextualizing data modifications and their downstream effects. The tool integrates directly with development workflows to streamline data deployment processes.
2. The core value of Recce lies in its ability to reduce manual review time by 90% while ensuring data accuracy and compliance with best practices. It enables teams to verify data changes in real-world contexts, automate validation steps, and collaborate effectively on data deployments. This transforms data validation from a bottleneck into a strategic advantage for organizations.

Main Features

1. Recce automatically scans dbt code and data pipelines to detect modifications across models, schemas, and dependencies during pull requests. It generates visual lineage maps and impact summaries to highlight downstream effects of changes.
2. The platform provides contextual verification tools, including side-by-side data comparisons, statistical summaries, and automated anomaly detection for modified datasets. Users can validate changes against live production data or test environments.
3. Recce Cloud offers SOC-2 compliant collaboration features, enabling teams to share interactive reports, assign review tasks, and track verification statuses across stakeholders. It integrates with GitHub and other version control systems to embed checks directly into PR workflows.

Problems Solved

1. Recce addresses the challenge of time-consuming, error-prone manual reviews of dbt pull requests, which often lead to deployment delays or undetected data errors. Traditional methods lack automated impact analysis and contextual validation.
2. The primary users are data engineers, analytics engineers, and data platform teams working with dbt in organizations scaling their data operations. It also serves data leaders needing audit trails for compliance.
3. Typical scenarios include validating schema changes affecting 20+ downstream models, verifying metric calculations before financial reporting cycles, and auditing data pipeline modifications in regulated industries like healthcare or finance.

Unique Advantages

1. Unlike generic data observability tools, Recce specializes in pre-deployment validation with native dbt integration, offering granular lineage tracing at the column level. Competitors lack its PR-centric workflow automation.
2. The platform innovates with "live diff" functionality that compares data snapshots before and after changes, including statistical distributions and outlier detection. This goes beyond basic schema comparisons.
3. Recce's competitive edge comes from its turnkey implementation - teams can operationalize it in hours without engineering overhead. The combination of automated checklists, visual impact analysis, and secure collaboration is unmatched in open-source or commercial alternatives.

Frequently Asked Questions (FAQ)

1. How does Recce integrate with existing dbt workflows? Recce connects directly to dbt projects through CLI or cloud APIs, automatically parsing manifest.json files to map dependencies. It inserts review checklists as PR comments and supports both dbt Core and dbt Cloud environments.
2. Can Recce handle large-scale data pipelines with thousands of models? Yes, Recce uses incremental processing and column-level impact analysis to efficiently audit changes in complex environments. The engine prioritizes critical paths and downstream dependencies to avoid performance bottlenecks.
3. What security measures protect sensitive data during validations? Recce Cloud employs field-level encryption for credentials, SOC-2 compliant data handling, and optional on-prem deployment. Validation checks operate on metadata summaries unless explicitly configured for sample data inspection.
4. Does Recce support custom validation rules for specific business needs? Users can extend base functionality with Python plugins to implement organization-specific checks, including custom SQL assertions, data quality thresholds, and compliance requirements.
5. How does the pricing model scale for growing teams? Recce offers per-user licensing for small teams and consumption-based pricing for enterprises, with free tiers available for open-source contributors and community projects.