Buying snapshot
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| Decision point | What Foundational states |
|---|---|
How lineage is built | From source code, mapped field to field. For systems not defined in code, such as warehouses and BI tools, scheduled scans of metadata, query logs and schema information are merged into the same lineage graph. |
Code it reads | SQL, Python, Java, Scala, COBOL, TypeScript, Go and more, with dbt, Spark, Airflow and SQLAlchemy, and BI tools such as Looker, Tableau and Power BI. The documentation also lists .NET/C#, PHP, JavaScript, Bash, C/C++, Ruby and Rust. |
Git platforms | GitHub, GitLab, Bitbucket and Azure DevOps, including GitHub Enterprise Server and Cloud, GitLab Enterprise Edition and Bitbucket Data Center. |
Data access | Code and metadata by default, with no access to data, PII or PHI. Foundational accesses customer data only if you turn on data observability checks that validate the underlying data, such as null counts or distinct values. |
Setup and time to value | Foundational says setup takes under an hour — authenticate it to the relevant code repositories and any BI tools — with no code changes, integration work, custom engineering or professional services. Its Collibra comparison page says teams typically see value within two to four weeks, often starting with high-risk pipelines. |
Export to a catalog | Foundational can export its computed table- and column-level lineage into Collibra: it writes files to an S3 bucket in your AWS account, and Collibra Edge reads them with its OpenLineage (Cloud) capability. The documented setup takes about 40 minutes. |
Who it is built for | The Collibra comparison page says Foundational is designed for data, analytics and ML engineering teams responsible for CI/CD and change management, and that business users do not work in the code-level interface. |
Security and compliance | SOC 2 Type II compliant, with SSO and SAML authentication. |
Our take
Choose Foundational when the immediate problem is knowing what a code change will break in dashboards and models before it merges, and the people who need that answer are the engineers writing the code. If your first need is a business glossary or a stewardship workflow for business users, check that need with the vendor before you shortlist it.
Evaluate Foundational
Comparisons
Where it stands out
The author sees the impact before merge. Every code change gets an impact analysis on its pull request, surfaced there as a "Foundational Data Issue Analysis" check that can be configured to block merges of faulty code.
COBOL and cloud in one graph. Mainframe lineage from COBOL, copybooks, JCL, Db2 and BMC is linked with Java, cloud data platforms and BI dashboards, so one impact check can cover a COBOL field and the dashboards that depend on it.
A narrower access review by default. Foundational reads code and metadata, so an access review starts from repository and metadata permissions rather than data permissions.
Trade-offs to weigh
Observability checks need data access. The code-and-metadata access model covers lineage and pull request checks. Data observability checks that validate the underlying data, such as null counts, need access to customer data, so plan a separate access review before you turn them on.
Confirm what connects. Foundational's comparison page with Collibra says it needs no access to production warehouses, live databases or BI tools. Its documentation describes scheduled scans of warehouses and BI tools for metadata, query logs and schema. Ask which connections your rollout needs.
Lineage reaches Collibra on its own schedule. The export interval has a minimum and default of 14 days, and setup needs an S3 bucket you own, a Collibra Edge site in healthy status and the Technical Lineage permission. If your users read lineage in Collibra rather than in Foundational, plan for that cadence and that AWS work.
Cost follows the size of the graph. Adding analysts does not change the price, while adding systems does.
Pricing and plan conditions
Foundational's pricing page says every plan includes the full platform: cross-platform lineage, impact analysis, data quality, data contracts and AI governance. It prices by the number of tracked objects in the lineage graph, not by seats, and cost grows with the overall complexity of the systems represented in that graph. To reach the vendor, you submit a form with first name, last name, company email, company name and phone number.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
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Alternatives to Foundational
The reviewed substitutes for Foundational among the data catalogs, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Atlan
- Foundational suits data and platform engineering teams whose immediate problem is column-level lineage from source code and checking changes in pull requests; Atlan fits teams that want a catalog as the main workspace.Applies to: Technical lineage and change safety on pull requests versus a catalog-led governance workspace
- Collibra
- Foundational suits data and platform engineering teams whose immediate problem is column-level lineage from source code and checking changes in pull requests; Collibra fits teams that want a catalog as the main workspace.Applies to: Technical lineage and change safety on pull requests versus a catalog-led governance workspace