| Pattern | Description | Quality Impact | | :--- | :--- | :--- | | | Store contracts in Git (YAML/JSON) and version them. | Enables peer review of schema changes before deployment. | | Ingestion Gateways | Use a lightweight service (e.g., Kafka with schema validation) to enforce contracts during ingestion. | Blocks bad data 100% before it lands in the data lake/warehouse. | | Automated Contract Testing | In CI/CD, run tests that mock producer data against the contract. | Catches breaking changes before they reach production. | | Contract Registry | A centralized UI/API where all teams discover and subscribe to contracts. | Reduces shadow pipelines and duplicate ETL logic. |
that covers the entire lifecycle from design to enforcement. Data Contracts 101 PDF | Pattern | Description | Quality Impact |
Use a simple YAML format initially. Include: | Blocks bad data 100% before it lands
As data becomes increasingly critical to business decision-making, ensuring data quality has become a top priority for organizations. However, achieving high-quality data is not a straightforward task, especially in today's complex data ecosystems. This is where data contracts come in – a powerful tool for driving data quality and reliability. | | Contract Registry | A centralized UI/API
Andrew Jones Core Premise: Moving from "trust on ingestion" to "trust by design" using software engineering principles for data.
Traditional data quality approaches are often reactive, catching errors only after they have corrupted dashboards or AI models. Data contracts drive quality through several key mechanisms: