OpenDP
OpenDP is a governed data control plane built on Apache Airflow, OpenMetadata, and OpenLineage. It standardizes ingestion, orchestration, quality, lineage, and delivery across analytics and AI workloads, evaluating data contracts and quality gates as part of the delivery lifecycle rather than after deployment.
OpenDP standardizes data delivery through open interfaces and reusable controls while preserving your existing warehouse, lakehouse, streaming, orchestration, and cloud investments. It integrates Apache Airflow for orchestration, OpenMetadata for governance, and OpenLineage for lineage tracking into a single control plane, so analytics and AI teams build, ship, and monitor reliable data solutions through self-service templates without duplicating underlying storage investments or locking into a single vendor stack.
The operating challenge
Data platforms accumulate different ingestion patterns, transformation frameworks, deployment practices, ownership models, and quality controls as teams and workloads grow.
What OpenDP provides
OpenDP unifies your modern data stack, integrating Apache Airflow orchestration, OpenMetadata governance, and OpenLineage tracking, into a single, governed control plane. It enables analytics and AI teams to build, ship, and monitor reliable data solutions through self-service templates without duplicating your underlying storage investments.
How adoption starts
Map critical data flows and operational pain points, establish a small set of reference patterns, and introduce reusable controls around the highest-value workloads first.
Open standards first
Native integration with OpenMetadata, OpenLineage, and Apache Airflow avoids vendor lock-in and keeps the underlying storage and compute choices open.
Declarative governance
Data contracts, quality gates, ownership, lineage, schema controls, and policy requirements are evaluated as part of the delivery lifecycle rather than added after deployment.
Unified control
Manage data solutions and pipeline standards consistently across environments while allowing storage, compute, catalog, and orchestration technologies to evolve independently.
Who it is for
Data and platform engineering teams that need repeatable, governed delivery of data pipelines, data solutions, quality controls, and shared platform capabilities.
When not to use it
When a small analytics environment or an existing managed data platform already provides sufficient governance, automation, and delivery standards.