The Shift Left Data Manifesto maintains that since data originates from application logic, expectations about data should be asserted in the code base. Tools like static analysis help identify data-producing structures, while data contracts ensure shape, semantics, lineage, and ownership are regularly checked. Change-impact analysis alerts developers to potential issues that may arise from changes, and policy as code governs compliance at build time. Platforms such as Gable enable these practices by scanning repositories to draft contracts and manage dependencies efficiently, aligning accountability with developers.
A growing movement reframes the problem: data is code. 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, not downstream.
Static analysis parses application code to identify data-producing structures before execution. Data contracts define shape, semantics, lineage, and ownership, which are checked automatically in continuous integration (CI).
Change-impact analysis warns developers when an innocuous refactor will break a machine learning feature downstream. Policy as code for governance evaluates compliance rules at build time rather than audit time.
New software platforms like Gable, scan source repositories to identify tables, events, or documents that the code will create. Notifications target the same developer who opened the pull request, aligning accountability with control.
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