ODD: Next-Gen Data Discovery and Data Observability Platform

ODD: Next-Gen Data Discovery and Data Observability Platform

What is ODD or Open-Source Data Discovery?

ODD (Open-Source Data Discovery) is a powerful, self-hosted, open-source tool designed to help data teams streamline and democratize access to data. It cuts down the time spent searching for data by providing a modern, intuitive interface that makes discovering datasets fast and easy.

Beyond discovery, ODD brings transparency by tracking who uses what data and how, enabling accountability and trust. It actively supports a healthy data culture by continuously monitoring data quality and compliance, reducing risks and manual overhead.

Ultimately, ODD accelerates insights by turning chaotic data exploration into a structured, collaborative, and reliable process, empowering teams to act faster with confidence.

Features

  • Federated Discovery: Instant search across all data sources via a unified catalog.
  • End-to-End Lineage: Visualize data flow from source ingestion to final dashboards.
  • ML-First Design: Auto-log experiment parameters and link models to data.
  • Governance & Security: Granular access control, compliance tagging, and usage auditing.
  • Data Quality: Real-time monitoring with support for dbt and Great Expectations.
  • Reference Data Hub: Centralized management for lookup tables (currencies, codes).
  • Collaborative: Safer deprecation workflows with downstream risk analysis.
  • Open & Private: Fully extensible, self-hosted, and privacy-first architecture.

Available Integrations

  • Airflow
  • Airflow 2+
  • Apache Druid
  • Cassandra
  • ClickHouse
  • Elasticsearch
  • Hive
  • Kafka
  • Feast
  • MSSQL
  • MySQL
  • Microsoft ODBC
  • MongoDB
  • Neo4j
  • MariaDB
  • Oracle
  • PostgreSQL
  • Redshift
  • Snowflake
  • Vertica
  • Tarantool
  • Athena
  • DynamoDB
  • Glue
  • Kinesis
  • Quicksight
  • S3
  • SageMaker
  • SageMaker Feature Store
  • SQS
  • Delta Lake (S3)
  • Tableau
  • Cube
  • Superset
  • Power BI
  • Trino
  • Presto
  • DBT
  • Redash
  • Spark
  • MLflow
  • Kubeflow
  • Databricks Unity Catalog
  • Great Expectations
  • SQLite
  • Couchbase
  • CockroachDB
  • Fivetran
  • Airbyte
  • Metabase
  • Mode
  • BigQuery
  • SingleStore
  • BigTable
  • Google Cloud Storage
  • Blob Storage
  • DuckDB
  • ScyllaDB

License

Apache-2.0 License.

Resources & Downloads

GitHub - opendatadiscovery/odd-platform: First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.
First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business. - opendatadiscovery/odd-platform
Open Data Discovery
First Open-Source Data Discovery and Observability Platform

Similar Apps

Coroot: A Libre, Self-Hosted APM and Observability System – A Free Alternative to DataDog and NewRelic
Monitoring modern infrastructure can feel like trying to solve a Rubik’s Cube in the dark. Between microservices, containers, and distributed systems, keeping tabs on everything is no small feat. That’s where Coroot steps in—a game-changing, open-source APM (Application Performance Monitoring) and observability tool that flips traditional monitoring
Elementary - A Powerful Open-source Solution for Data Observability
If you’re a data engineer or data scientist, you understand the importance of a robust data observability tool. Enter Elementary, a native data observability solution designed specifically for data and analytics engineers. It’s not just a tool, it’s a comprehensive platform that integrates seamlessly with dbt, allowing you to set
8 Open-Source Platforms to Add Observability to Your LLM Applications (No Vendor Lock-In)
As Large Language Models (LLMs) power everything from customer support chatbots to internal coding assistants, reliability, transparency, and performance have become non-negotiable. But unlike traditional software, LLMs are inherently probabilistic—making them prone to hallucinations, latency spikes, and unexpected behavior. That’s where LLM observability comes in. And if you

Read more