Designs a physical data model for the target platform without introducing anomalies

https://knobit.org/id/outcome/LO-6

dcterms:description	Stage 4 (Physical model) of the data-modelling skill: Designs a physical data model for the target platform without introducing anomalies; produces 04-physical/physical-model.yaml. en
dcterms:identifier	LO-6
dcterms:license	https://creativecommons.org/licenses/by/4.0/
has prerequisite	Derives a normalised logical data model and traces anomalies to their causes
includes knobit	Recognises a constraint claimed by a rule and missing from the schema
includes knobit	Recognises a declared constraint the target platform does not enforce
includes knobit	Recognises an unmanaged duplicate column
includes knobit	Recognises logical entities merged into one table
includes knobit	Recognises a stored derived or summary value
includes knobit	Recognises a lossy or unconstrained vertical split
includes knobit	Recognises an entity split into clone tables by value
includes knobit	Recognises rolled-up subtypes losing conditional constraints
includes knobit	Recognises rolled-down subtypes losing supertype constraints
includes knobit	Recognises a hierarchy encoded redundantly
includes knobit	Recognises content held outside the database by reference
includes knobit	Recognises a surrogate key without natural-key uniqueness
includes knobit	Recognises a surrogate value with meaning, reused or renumbered
includes knobit	Recognises an undeclared foreign key
includes knobit	Recognises a referential action destroying what should survive
includes knobit	Recognises a changeable value list fixed in the DDL
includes knobit	Recognises a physical domain differing from the logical domain
includes knobit	Recognises binary floating point used for an exact quantity
includes knobit	Recognises a business fact held in physical row order
includes knobit	Recognises an index set not derived from access paths
includes knobit	Recognises an overwrite where history is required
includes knobit	Recognises inconsistent validity periods in a versioned table
includes knobit	Recognises a reference to a versioned table by business key alone
includes knobit	Recognises a soft delete the constraints do not understand
includes knobit	Recognises a fact table of mixed grain
includes knobit	Recognises a measure without stated additivity
includes knobit	Recognises a nullable foreign key on a reporting join path
includes knobit	Recognises a sentinel value standing in for null
includes knobit	Recognises a null with a business meaning
includes knobit	Routes a defect to the deliverable responsible for it
includes knobit	Explains controlled denormalisation
includes knobit	Explains the implementation options for subtypes
includes knobit	Explains referential integrity and referential actions
includes knobit	Explains exact and approximate numeric types
includes knobit	Explains null semantics and three-valued logic
includes knobit	Explains value sets held as reference data
includes knobit	Explains index design from access paths
includes knobit	Explains history strategies and validity-period conventions
includes knobit	Explains grain and additivity in dimensional models
includes knobit	Explains vertical and horizontal partitioning of tables
includes knobit	Explains consistency of content stored by reference
includes knobit	Explains the absence of row order in relational tables
includes knobit	Designs a star schema for a stated grain
includes knobit	Designs a Data Vault model for auditable history
includes knobit	Designs a document structure for a stated access pattern
includes knobit	Designs views hiding physical structure from consumers
includes knobit	Recognises history kept without its constraints checked
owl:sameAs	https://bitbucket.org/reffa/ontology/id/outcome/data-modelling/06-physical
rdfs:label	Designs a physical data model for the target platform without introducing anomalies en
rdfs:label	Kujundab sihtplatvormile füüsilise andmemudeli uusi anomaaliaid tekitamata et
related topic	Data modelling and design (DAMA-DMBOK2 chapter 5)

Used by

assesses learning outcome	Generated DDL review, Postgres and Snowflake (DDL review skills)
assesses learning outcome	Seven retail banking designs in seven DMBOK2 modelling schemes (scheme design skills)
assesses learning outcome	Data modelling knowledge: Physical design and DDL (CDMP-style questions)
assesses learning outcome	Retail banking servicing schema on PostgreSQL (transactional physical design anomaly skills)
assesses learning outcome	Loan application physical model on Snowflake (physical design anomaly skills)
has prerequisite	Reviews a data model against the Data Model Scorecard, with evidence, an independent reviewer and a decision that follows the score
has prerequisite	Reverse-engineers an as-is model from a database, separating what is declared from what is inferred
has prerequisite	Generates DDL per target dialect and reviews it against model, engine, data and deployment
includes learning outcome	Learning path: Data modelling
related learning outcome	Stage 6 — The physical data model
related learning outcome	Physical model anomaly checklist
schema:item	https://knobit.org/id/path/data-modelling/LO-6