Grai

Grai is a data-lineage tool for tracing how information connects across databases, warehouses, APIs, and dashboards, and for testing those relationships. It can automatically map lineage at the column level across warehouses and production services. Centralized validation tests can run when an upstream source changes, while GitHub Actions can alert CI/CD workflows when production changes affect warehouses or dbt projects.

Connectors listed include Snowflake, BigQuery, Redshift, Postgres, MySQL, SQL Server, dbt, Fivetran, flat files, Metabase, and Looker; the Looker connector is marked alpha. Grai is open source and can be self-hosted, with Docker Compose and Kubernetes examples and Helm charts. Grai Cloud is available for free. The web application uses React, and its CLI is a Python client for the Grai server. The listed license is MIT-0. Grai’s privacy policy says it collects email addresses, cookies, and usage data, and cannot guarantee absolute security.

Grai combines lineage mapping with validation and change alerts across listed data systems. Its free cloud option and self-hosting support provide different deployment choices.

Grai suits people who need to trace data relationships across systems and validate how changes affect downstream data. It offers both self-hosting and a free cloud option.

Grai is a data lineage and validation tool for teams connecting databases, warehouses, APIs, and dashboards. It is best suited to data practitioners who need to trace dependencies and catch the effects of upstream changes in tests or CI/CD. Its free cloud and self-hosting options make it worth considering, provided its connectors match your stack.

Overview

Grai brings automated discovery, column-level lineage, impact analysis, pipeline lineage, and report lineage together. Rather than stopping at a map of data relationships, it also supports centralized validation tests that run when upstream sources change. That combination suits teams that need to understand dependencies and respond to changes; for teams seeking only a basic inventory, the testing and workflow capabilities may be more than they need.

Grai is open source under an MIT-0 license and can be self-hosted, with Docker Compose and Kubernetes examples and Helm charts. Grai Cloud can also be run for free. Self-hosting offers control over the hosting environment, while cloud avoids running the service yourself; either way, the practical fit depends on connector coverage.

Key features

• Automated column-level lineage: Grai says it can map data relationships across warehouses and production services. Column-level detail can help teams follow dependencies more precisely than a broad system-to-system view.
• Validation on upstream changes: Centralized tests run when upstream sources change, giving teams a way to check downstream data when dependencies shift.
• CI/CD alerts: GitHub Actions can alert CI/CD workflows when production changes affect warehouses or dbt projects. This is useful for teams that want change awareness in delivery workflows, though its reach is framed around those systems.
• Connectors: Listed integrations include Snowflake, BigQuery, Redshift, Postgres, MySQL, SQL Server, dbt, Fivetran, flat files, Metabase, and Looker. Looker is marked alpha, so teams relying on it should treat that integration as less mature than the others.
• Developer access: The web application is built with React, and the Grai CLI is a Python client library for interacting with the server. This offers both a web interface and a programmatic route for teams working with the service.

Pricing

Grai Cloud costs nothing. The free plan is the only plan described, so it is a straightforward starting point for teams that want cloud access without a subscription. No paid tier or plan-specific usage cap is given, so teams with requirements beyond the free option should confirm whether it meets their operational needs before committing to it as a long-term deployment.

Self-hosting is also free as open-source software under the MIT-0 license. It avoids a cloud subscription but means operating the deployment yourself; Docker Compose and Kubernetes examples and Helm charts provide deployment paths. Choose cloud if you want to avoid running the service, and self-hosting if control of the hosting environment matters more.

Platforms

Grai supports API, Linux, self-hosted, and web use. Its web application is built with React, and its Python CLI provides a client for interacting with the server. The combination suits teams that want web-based access alongside programmatic interaction, while self-hosting gives Linux-oriented teams a deployment option.

Who it's for

Grai is a fit for data teams that need to trace data relationships across databases, warehouses, APIs, and dashboards, then validate changes upstream. Its lineage, tests, and GitHub Actions are most relevant when teams need to connect dependency visibility to their existing warehouse or dbt delivery workflows. Teams should check that the listed connectors cover their systems, especially if Looker is central to their work.

Pros and cons

• Pros:
  • Automated column-level lineage, impact analysis, and pipeline and report lineage support a detailed view of data dependencies.
  • Tests that run on upstream changes and GitHub Actions alerts connect lineage to validation and CI/CD workflows.
  • Free cloud and self-hosting offer a choice between avoiding deployment work and retaining hosting control.
  • The connector list spans warehouses, databases, data tooling, and dashboards.
• Cons:
  • The Looker connector is alpha, which is a meaningful caveat for teams that depend on it.
  • Self-hosting requires teams to operate their own deployment rather than relying on the cloud option.

Alternatives

If you want a free, open-source lineage option without a cloud-versus-self-hosting choice tied to Grai, compare Apache Atlas, which is described as free metadata governance software with no paid plans or usage limits stated. OpenMetadata is another free self-hosted option, with no usage limits stated. For a different balance of catalog and transformation features, Coalesce Catalog has a free one-user Developer plan with Transform and Quality, 2,000 capped monthly actions, one project, and one environment; its Starter plan costs $150 a month. Databricks Notebooks offers a free edition with one serverless workspace and limited compute size and usage, with a pay-as-you-go option. Amazon DataZone uses pay-as-you-go billing based on requests, metadata storage, compute, and AI recommendation tokens, with no upfront fees or long-term commitments. Atlas describes itself as open-core, with an Apache 2.0 core engine and advanced features in proprietary editions. Collate pairs a fully open-source OpenMetadata core with a Community License for its UI and connectors. Google Cloud Knowledge Catalog is a paid option without a free plan.

For more options, see Data Lineage Tools.

Verdict

Choose Grai if your team needs column-level lineage tied to upstream validation and CI/CD alerts, and its connectors cover your systems. Its free cloud option and self-hosted open-source deployment are strong reasons to evaluate it; look elsewhere if a critical integration such as Looker needs a more mature connector or you want a different catalog or governance model.