Open-Source Observability · opendatadiscovery

odd-platform

ODD Platform is an open-source data discovery and observability tool that centralizes metadata from scattered data sources, tracks data lineage, and monitors data quality across your entire data landscape. It helps data teams reduce discovery time, maintain compliance, and improve collaboration through a modern web interface.

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Source: GitHub — github.com/opendatadiscovery/odd-platform

1.4k

GitHub stars

141

Forks

Java

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	opendatadiscovery/odd-platform
Owner	opendatadiscovery
Primary language	Java
License	Apache-2.0 — OSI-approved
Stars	1.4k
Forks	141
Open issues	122
Latest release	0.29.0 (2026-06-26)
Last updated	2026-07-08
Source	https://github.com/opendatadiscovery/odd-platform

What odd-platform is

Java-based platform that aggregates metadata from 25+ data sources via collectors, stores metadata in PostgreSQL, provides end-to-end data lineage tracking, integrates with Great Expectations and dbt for quality monitoring, and exposes functionality through a REST API and React-based UI.

Quickstart

Get the odd-platform source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/opendatadiscovery/odd-platform.git2cd odd-platform3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-source Data Catalog & Discovery

Consolidate metadata from heterogeneous data systems (databases, data warehouses, lakes, message brokers) into a single searchable catalog, reducing time spent locating and understanding data assets across teams.

Data Lineage & Impact Analysis

Track end-to-end data flow through pipelines and transformations to understand data dependencies, assess impact of schema changes, and responsibly deprecate outdated objects by quantifying downstream usage.

Data Quality & Observability Monitoring

Integrate quality checks from dbt and Great Expectations, set up alerts on quality metrics, and maintain a dashboard view of data health trends to catch issues early and prevent bad data from flowing downstream.

Implementation considerations

Requires PostgreSQL instance and Docker/Kubernetes infrastructure; plan for network access to target data sources and secure credential storage for collectors.
Each data source needs a dedicated collector container or adapter; no single agent covers all sources. Assess which integrations you need and whether custom adapters must be developed.
Metadata freshness depends on collector scheduling (hourly/daily typical); design expectations around how stale metadata is acceptable for your lineage and governance workflows.
Data lineage requires instrumentation or integration with transformation tools (Airflow, dbt); auto-discovery is limited to schema/table metadata, not business logic flows.
No built-in authentication/authorization shown in README; verify identity model and RBAC capabilities match your org's access control requirements.

When to avoid it — and what to weigh

Need Enterprise SLA/Support — This is community-driven open-source. No vendor guarantees response times, uptime, or production support. Requires in-house expertise for troubleshooting and operational issues.
Lightweight Deployment Required — Requires PostgreSQL backend, Docker/Kubernetes orchestration, and ongoing management of collectors and microservices. Not suitable for minimal-footprint, single-binary deployments.
Real-time Streaming Metadata Updates — Collectors push metadata on schedules; platform is not designed for sub-second metadata freshness. Better suited for hourly/daily metadata syncs rather than live event-driven updates.
Proprietary Data Governance Standards — Implementation is tailored to Open Data Discovery Spec. If your org requires locked-in proprietary governance workflows or models, integration effort will be significant.

License & commercial use

Apache License 2.0. This is a permissive OSI-approved license that allows commercial use, modification, and distribution with proper attribution and liability disclaimers.

Apache 2.0 explicitly permits commercial use without purchase or license agreement. No proprietary clauses restrict production deployment. However, you assume all operational risk and must handle security, compliance, and support independently. No warranty is provided.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No security audit, vulnerability disclosure, or hardening details in provided data. Operational considerations: collector processes need credentials for target systems (must be injected securely); PostgreSQL backend requires network isolation and authentication; no authentication/authorization model described in README. Assess data sensitivity of what metadata will be stored and who can access it. Review RBAC implementation before production use.

Alternatives to consider

Collibra

Proprietary, enterprise-grade data governance with superior support, pre-built workflows, and stronger regulatory compliance features. Requires budget and locks you into a vendor.

Informatica Catalog

Commercial metadata and data discovery platform with broader ecosystem integrations and professional services. Higher cost but vendor-backed support and SLA guarantees.

Alation

SaaS-first data catalog with AI-driven curation, community features, and strong metadata management. Simpler deployment but subscription-based with data residency considerations.

Software development agency

Build on odd-platform with DEV.co software developers

ODD Platform is a solid open-source foundation for data teams prioritizing metadata consolidation and lineage tracking. Evaluate it for your use case, assess deployment and support readiness, and consider custom development needs for your specific integrations.

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odd-platform FAQ

Does ODD Platform automatically discover lineage?

Partial. Schema/table metadata is discovered, but end-to-end lineage requires integration with transformation tools (dbt, Airflow) or API instrumentation. Manual lineage definition is also supported.

What happens if I lose my PostgreSQL database?

All metadata stored in PostgreSQL will be lost. Back up your database regularly and ensure your collectors can re-sync metadata to rebuild the catalog. Business-critical metadata should be versioned externally.

Can I run ODD Platform on-premises?

Yes. Docker Compose and Kubernetes Helm deployments are documented. You manage all infrastructure, networking, and security. No cloud SaaS option is mentioned in the data provided.

Does ODD have built-in data quality rules or only integrate third-party tools?

Platform is integration-first: it accepts quality results from dbt, Great Expectations, and custom frameworks via API. No native quality engine is mentioned. You must bring or build quality checks externally.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If odd-platform is part of your open-source observability roadmap, our team can implement, customize, migrate, and maintain it.

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