Data is being reframed as code, which shifts the responsibility for data management to the development stage. To ensure quality, static analysis can parse application code to detect data-producing elements before execution. Data contracts provide definitions for data attributes and are validated during continuous integration. Additionally, change-impact analysis helps identify potential downstream issues from code changes. New platforms, like Gable, support this shift by analyzing source code and improving data governance, ultimately placing change management responsibilities with developers.
Every record begins life in application logic: a TypeScript event, a Java entity, a Python variable. If code produces the data, the correct place to assert expectations is inside the code base.
Static analysis parses application code to identify data-producing structures before execution, ensuring that changes to data are managed alongside software changes.
Data contracts define shape, semantics, lineage, and ownership, which are checked automatically in continuous integration (CI), enhancing the reliability of data management.
Change-impact analysis warns developers when an innocuous refactor will break a machine learning feature downstream, preventing unexpected failures in production.
Collection
[
|
...
]