首个开源的数据发现和可观测性平台。我们为数据从业者提供便利，让您可以专注于业务。
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## Demo Play with our [demo app](https://demo.oddp.io)! ## Introduction ODD is an open-source data discovery and observability tool for data teams that helps to efficiently democratise data, power collaboration and reduce time on data discovery through modern user-friendly environment. ### Key wins * Shorten data discovery phase * Have transparency on how and by whom the data is used * Foster data culture by continuous compliance and data quality monitoring * Accelerate data insights * Know the sources of your dashboards and ad hoc reports * Deprecate outdated objects responsibly by assessing and mitigating the risks * :point_right: ODD Platform is a reference implementation of **[Open Data Discovery Spec](https://github.com/opendatadiscovery/opendatadiscovery-specification)** ## Features ### Data Discovery and Observability * Accumulate scattered data insights in Federated Data catalogue * Gain observability through E2E Data objects Lineage * Benefit from cutting-edge E2E microservices Lineage feature in tracking your data flow through the whole data landscape * Be warned and alerted by Pipeline Monitoring tools * Store your metadata * Use ODD-native modern lightweight UI ### ML First citizen * Save results of your ML Experiments by automatically logging its parameters ### Data Security & Compliance * Manage Tags to prevent any abuse of the data * Refer to Tags to stay compliant with data security standards * Have full transparency on how and by whom the data is used ### Data Quality * Utilize advanced Data Quality Dashboard to gain insights into data quality metrics, trends, and issues across your datasets, enabling proactive data quality management * Simplify DQ processes by using ODD with Great Expectations and DBT tests compatibility * Integrate ODD with any custom DQ framework ### Reference Data Management (Lookup Tables) - a part of Master Data Management (MDM) * Manage and store reference data centrally, ensuring a single source of truth for key data elements like currency codes, country names, and product categories, etc. * Easily integrate Lookup Tables with data pipelines and transformations, enhancing data enrichment and validation processes * Support data governance and compliance efforts by maintaining accurate and consistent reference data across all data assets ## Getting Started ### Running as a separate container Setting up PostgreSQL connection details, for example: ```shell export POSTGRES_HOST=172.17.0.1 export POSTGRES_PORT=5432 export POSTGRES_DATABASE=postgres export POSTGRES_USER=postgres export POSTGRES_PASSWORD=mysecretpassword ``` Starting new instance of the platform: ```shell docker run -d \ --name odd-platform \ -e SPRING_DATASOURCE_URL=jdbc:postgresql://${POSTGRES_HOST}:${POSTGRES_PORT}/${POSTGRES_DATABASE} \ -e SPRING_DATASOURCE_USERNAME=${POSTGRES_USER} \ -e SPRING_DATASOURCE_PASSWORD=${POSTGRES_PASSWORD} \ -p 8080:8080 \ ghcr.io/opendatadiscovery/odd-platform:latest ``` Go to [localhost:8080](http://localhost:8080) in case of local environment. ### Running Locally with Docker Compose ```shell docker-compose -f docker/demo.yaml up -d odd-platform-enricher ``` * :point_right: **[QUICKSTART](./docker/README.md)** ### Deploying to Kubernetes with Helm Charts * :point_right: **[QUICKSTART](https://github.com/opendatadiscovery/charts/blob/main/QUICKSTART.md)** ### Example configurations There are various example configurations (via docker-compose) within **[docker/examples directory](https://github.com/opendatadiscovery/odd-platform/tree/main/docker/examples)**. ## Contributing Contributing to ODD Platform is very welcome. For basic contributions, all you need is being comfortable with GitHub and Git. The best ways to contribute are: * Work on new adapters * Work on documentation To ensure equal and positive communication, we adhere to our [Code of Conduct](./CODE_OF_CONDUCT.md). Before starting any interactions with this repository, please read it and make sure to follow. Please before contributing check out our [Contributing Guide](./CONTRIBUTING.md) and issues labeled "good first issue":暂无开放 Issues，或尚未同步最近议题。