Zero-Data-Access governance. Prevent breaking changes before they reach Collibra.
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Foundational analyzes source code in Git to prevent data incidents before deployment—governing SQL, Python, dbt, and Spark changes before they reach production.
Collibra catalogs production metadata after deployment through 100+ connectors to provide enterprise-wide search, stewardship workflows, and governance documentation.
Foundational is build-time governance that analyzes uncommitted source code changes in Git before merge to prevent breaking changes. Collibra is a runtime data catalog that harvests metadata from production systems after deployment to support discovery, documentation, and stewardship workflows.
Foundational helps teams prevent incidents before deployment through pre-merge validation and automated governance in Git. Collibra helps organizations catalog, discover, and govern data assets after deployment through enterprise metadata management.
Choose Foundational if your goal is preventing schema breaks, data contract violations, and ML model failures through build-time validation and automated enforcement before code reaches production.
Choose Collibra if your goal is creating an enterprise-wide data catalog with comprehensive stewardship workflows, business glossaries, and searchable metadata across hundreds of production sources.
Foundational integrates directly into Git workflows to analyze code changes during pull requests. The platform parses uncommitted diffs to detect schema changes, contract violations, and downstream impacts while prevention is still possible.
Collibra connects to production systems to extract metadata and populate a centralized catalog. Teams use Collibra to search for assets, document systems, and manage stewardship workflows across deployed data environments.
Foundational operates at build time, analyzing code changes before they're deployed. When a data engineer commits a schema modification, the platform immediately:
This happens during the pull request phase—when preventing issues is still possible and cost-effective. Teams catch breaking changes during code review rather than in production war rooms. The automated enforcement ensures governance policies are applied consistently without manual intervention.
Engineers receive immediate feedback in pull requests showing exactly which downstream systems would be affected, which contracts would be violated, and which teams need to coordinate before deployment. Issues caught at this stage can be fixed in minutes rather than hours or days.
Collibra catalogs data after deployment through connectors that harvest metadata from production systems. The platform:
This approach excels at documenting what exists in production and providing business context for discovery. Teams use Collibra to understand available data, find approved datasets, and coordinate governance activities across the enterprise. The platform supports compliance by maintaining comprehensive documentation trails.
However, the catalog cannot prevent future changes from breaking downstream systems since the analysis occurs after code is deployed. Issues are discovered through monitoring rather than prevented during development.
Foundational asks "What will break if we merge this change?" during pull request review. Collibra asks "What exists in production and how do we find it?" after deployment.
Foundational achieves complete accuracy by analyzing the actual code that defines data transformations:
Column-level lineage is extracted directly from code logic—no sampling, no inference, no gaps. When code changes, lineage updates instantly because it's derived from the source of truth: the code itself. There's zero maintenance required; lineage accuracy is guaranteed by analyzing syntax rather than inferring from execution patterns.
The platform can show lineage for uncommitted changes, enabling predictive impact analysis before deployment. Developers see exactly which downstream columns and transformations will be affected by proposed code changes.
Collibra builds lineage by analyzing query logs, metadata schemas, and execution patterns from production systems:
This approach works well for documenting production flows and providing business context for existing connections. The lineage integrates with Collibra's broader catalog, enabling users to understand how data flows through operational systems.
Limitations include potential coverage gaps for code-defined lineage that hasn't executed, reliance on scheduled updates rather than real-time accuracy, and inability to predict impact of future changes since analysis occurs on deployed systems.
Foundational's source-code lineage enables "what if" analysis on proposed changes before deployment. Collibra's runtime lineage documents "what is" currently connected in production after deployment.
Foundational embeds directly into GitHub and GitLab workflows as a native part of the development process:
Developers see governance results immediately in the tools they already use daily. There's no separate governance platform to log into, no tickets to file, no manual reviews to wait for. Governance becomes a seamless, automated part of the development workflow rather than a separate process that slows velocity.
The automated enforcement is critical: if a change would break a downstream ML model or violate a data contract, Foundational blocks the merge. The issue must be resolved—either by fixing the breaking change, coordinating with downstream consumers, or explicitly accepting the risk through an override workflow—before code can reach production.
Collibra provides governance workflows and policy management through its web-based platform:
This model works well for business glossaries, policy documentation, and coordinated stewardship activities. Organizations establish formal governance processes with defined roles, responsibilities, and approval workflows.
However, the workflows require manual intervention and operate outside the development process. Data engineers must context-switch between development tools and governance platforms. Enforcement depends on humans following processes rather than automated gates that prevent deployment.
Foundational provides automated enforcement during development through CI/CD integration. Collibra provides stewardship workflows and policy documentation that operate on production systems.
Foundational optimizes for data engineers, analytics engineers, and software engineers who write and deploy code:
The platform speaks the language of engineering: commits, pull requests, merges, CI/CD pipelines, automated testing, deployment gates. It accelerates development velocity by catching issues early and providing actionable feedback directly in the development workflow.
Engineers appreciate that governance happens automatically as part of their normal workflow. There's no separate governance tool to learn, no manual processes to follow, no bureaucratic overhead. The platform makes governance invisible when everything is working correctly and only surfaces issues that actually need attention.
Collibra emphasizes accessibility for business analysts, data stewards, executives, and non-technical users:
The platform prioritizes discoverability and documentation over prevention, making it ideal for organizations where business users need self-service access to understand available data. Data stewards coordinate governance activities through formal workflows rather than automated enforcement.
The emphasis on business context, searchability, and comprehensive cataloging serves organizations focused on democratizing data access and documenting governance processes for compliance and audit purposes.
Proactive data governance that analyzes uncommitted source code changes to prevent issues before deployment. Foundational's active governance integrates directly into Git workflows, validating data contracts, performing lineage impact analysis, and blocking merges that would cause production failures—all before code reaches production environments. The platform operates at build-time, catching breaking changes during code review when fixing issues is fast and inexpensive.
Post-deployment approach that harvests metadata from production systems via connectors to build searchable enterprise catalogs. Collibra's platform collects metadata after data is deployed and operational, enabling discovery, documentation, and stewardship of existing data assets across the enterprise. The catalog provides business context, glossaries, and workflows that help organizations understand and govern their production data estate.
Foundational uses a usage-based pricing model tied to the specific pipelines and assets governed during the build process. Unlike data catalogs or observability tools that price per user seat or connector, Foundational scales with governed change rather than headcount. This approach ensures predictable costs even as data volume and team sizes grow.
Foundational typically delivers value within two to four weeks. Because it integrates directly into Git repositories and CI/CD pipelines, it does not require complex production system connections. Organizations often start by governing high-risk pipelines first to achieve immediate protection against breaking changes.
No. Foundational does not require access to production warehouses, live databases, or BI tools. It operates entirely by analyzing source code in Git to enforce governance standards before code is deployed. This "shift-left" architecture minimizes security risks and simplifies compliance in regulated industries.
Foundational replaces manual code reviews and reactive governance but is often used alongside catalogs for discovery. While catalogs focus on "what exists," Foundational focuses on "what is changing." Some teams eventually consolidate tools once Foundational’s source-code lineage and preventive controls meet their discovery and reliability needs.
Foundational is designed specifically for Data, Analytics, and ML Engineering teams responsible for CI/CD and change management. While business users do not interact with the code-level interface, they benefit from the resulting dashboard stability, trusted metrics, and the elimination of data downtime.
Modern data teams choose Foundational to move away from centralized, manual bureaucracy. Foundational enforces preventive controls automatically during development, allowing teams to move faster without sacrificing reliability or compliance.
Foundational empowers engineering teams to ship code faster with confidence—preventing production incidents through automated build-time governance that catches breaking changes, contract violations, and downstream impacts before merge, during the pull request phase when issues are easiest and cheapest to fix.
Collibra helps organizations catalog, discover, and govern data assets across the enterprise—harvesting metadata from production systems to enable self-service analytics, document business context, and coordinate stewardship activities through comprehensive workflows.
Choose based on your primary strategic objective: preventing incidents before deployment through build-time validation (Foundational) or cataloging and governing data after deployment through enterprise metadata management (Collibra). Most organizations choose one primary approach based on whether prevention or documentation is the strategic priority.
Explore how proactive governance creates measurable impact across your entire data lifecycle.
