Overview
Last updated
Last updated
The open-source Data Discovery Platform improves productivity, collaboration, and governance of modern data products and teams:
Free open-source and community-driven
ML first citizen
End-to-end microservices lineage support
Flexible data quality integration options
Auto-generated ML experiment lineage and metadata
To use Platform solutions more effectively apply . It contains best practices for managing your metadata. Its theoretical topics and examples help to build an effective data discovery process. Also the Spec covers data engagement and data federation problems.
ODD's target audience is any data team regardless of its' size. It can be enterprises or large-scale companies challenging data mesh concept, small and mid-scale companies seeking OSS data catalogue solution and also DS teams aiming at better data governance. An ecosystem for all data team members. Platform functionality covers requirements of Data Scientists and Engineers, Product and Project Managers as well as Data Analysts, Architects, QA and BI Engineers.
ODD can benefit a team at the following SDLC stages and team functions:
Every time a newcomer joins a data team, one faces a challenge of data management processes learning. Unfortunately, it may provoke overcommunication, reading irrelevant documentation and spending much time to pick out meaningful information. ODD solutions help figure out data sources, find owners of this data, check DBs structures and get many other attributes that are necessary for working activities.
The more data a team has the more complicated connections occur between assets and components. ODD provides a search tool with AI-powered suggestion and flexible filters. Also the Platform supports a description option, so you can get detailed info about data entities.
A problem of observability lack starts when irrelevant data appears in one's production cycle. To clarify when and where this data is and avoid delay use ODD's lineage diagram and flexible alert system.
Dive into ODD opportunities with a .
you can find most popular use cases for different titles of your data team.
