Kit Data Engineering

Data entities, dictionary, ISO/IEC 25012 quality requirements, retention, privacy classification, lineage, interfaces and migration

Classification levels and criteria, FIPS 199 impact method, personal and special-category flags, labelling, handling matrix incl. AI use, discovery, declassification

Lakehouse or warehouse architecture: consumers, ingestion, medallion layers, table formats, catalogue, OpenLineage 2-0-2 lineage, quality, security, cost

Latency SLOs, delivery guarantees, CDC sources, AsyncAPI 3.1.0 contracts, topics, watermarks, windows, state, exactly-once, capacity, lag

One data product: purpose, consumers, owner, input/output/management ports, ODCS contract links, SLOs, metadata, access, pricing, AI context, ODPS YAML

Producer-consumer data contract: schema, semantic roles, quality rules, SLA properties, servers and variables, roles, pricing, AI context, ODCS v3.2.0 YAML

Batch or streaming pipeline: sources, lineage, schedule, mapping, quality checks, backfills, volumes, runbook

Business and technical definitions per attribute: type, format, allowed values, source, owner, classification, code lists, derived fields

Organisation DQ standard: ISO/IEC 25012 vocabulary, tiers, rule catalogue, severity, enforcement points, observability, scorecard, issues, waivers

Retention rule method, deletion classes, standard periods, deletion per store (OLTP, Iceberg, Delta, search, logs, ML, backups), erasure propagation, evidence, sanitisation

Data incident postmortem: SEV1-SEV4, impact, timeline, lineage blast radius, decisions affected, backfill, SLO breach, GDPR check, DQ rule gaps