Engineering / advanced
Trustworthy datasets and data contracts
Make data ownership, validation, provenance, access, and retention executable parts of production data pipelines.
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Inside this module
Module 01 advanced
Trustworthy datasets and data contracts
Make data ownership, validation, provenance, access, and retention executable parts of production data pipelines.
- 01
Check schema compatibility in the actual reading direction
Build reader-writer compatibility tests while separating structural decoding, default values, and the meaning of AI training features.
24 min - 02
Measure quality with explicit rules and denominators
Combine row constraints, NULL semantics, profiling, and sampling boundaries before accepting a training-data quality percentage.
24 min - 03
Record the dependencies a run actually creates
Distinguish jobs, executions, datasets, columns, and indirect influences without treating a lineage diagram as complete proof.
24 min - 04
Pin immutable dataset contents and their interpretation
Distinguish mutable labels, snapshot references, byte identities, and reconstruction manifests for reproducible AI training.
24 min - 05
Turn classification into an enforced access decision
Connect dataset attributes, trusted request context, and resource enforcement without treating a catalog label as authorization.
24 min - 06
Track deletion through versions, derived data, and restore paths
Define a retention inventory and verifiable propagation process without equating a hidden object or completed job with complete removal.
24 min - 07
Bind allowed uses to records, purposes, and policy versions
Implement explicit use eligibility and revocation propagation for AI data without treating a consent flag or technical check as universal legal compliance.
24 min - 08
Reconcile identities and values at a defined cutoff
Compare source and derived data using authoritative fields, stable keys, aligned snapshots, and explicit late-data handling.
24 min - 09
Separate a data defect from a changed population
Investigate quality regressions with versioned evidence, slice-aware rates, controlled replay, and explicit containment decisions.
24 min - 10
Roll out a contract without hiding rejected or unchecked data
Stage observation, consumer validation, quarantine, enforcement, and rollback while retaining outcome accounting and historical-data scope.
24 min