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the_data_must_flow
the_data_must_flow contient 17 skills collectées depuis PatrickCuba, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Design and deploy an Activity Schema 2.0 pipeline as a Business Vault satellite. Covers BV staging transformation view, SAT_BV_NH_{ENTITY}_STREAM DDL, stream-on-view, triggered tasks, and per-activity IM Dynamic Tables.
Design and deploy Business Vault constructs — BV links and BV satellites. Covers delivery modes, rule views, staging, and doctrine rules.
Generate deployment artifacts for a Data Vault — schema DDL, dependency-ordered execution scripts, role/grant setup, and environment promotion.
Analyze source tables or schema descriptions and propose a Pragmatic Data Vault model (hubs, links, satellites)
Explain any Pragmatic Data Vault concept, pattern, rule, or modeling decision in plain language
Generate SQL DDL and load patterns for a validated Pragmatic Data Vault construct
Generate Snowflake orchestration (Tasks, DAGs, execution order) for vault loading. Handles dependency ordering, sequential same-hub loads, and ghost record deployment.
Build Information Mart views from the Raw Vault. Hash keys are never exposed — BI tools see only business keys and descriptive attributes.
Migrate an existing Data Vault from a legacy platform to Snowflake. Covers strategy selection (Lift & Shift / Lift & Adjust / Remodel), hash digest portability, PK/FK constraints, natural key refactoring, and parallel pipeline validation.
Design a specific Pragmatic Data Vault construct — hub, link, satellite, PIT, bridge, or same-as link. Subcommands: hub | link | satellite | pit | bridge | sal
When and how to build PIT tables and Bridge tables in DVOS — manifest config, materialisation options, bridge types, naming rules.
Design or review a DVOS staging view — source staging or BV staging. Covers metadata columns, hashkey/hashdiff computation, and the no-business-logic rule.
5-layer Supernova data modelling pattern as Snowflake INCREMENTAL Dynamic Tables above the Data Vault. Pre-materialises satellite joins, computed attributes, and data delivery shapes to eliminate query-time joins.
Generate Data Vault integrity tests — ad-hoc validation queries OR Snowflake DMF-based continuous monitoring with 17 reusable Data Metric Functions.
Validate a Pragmatic Data Vault model definition against Pragmatic DV doctrine rules. Subcommands: model | manifest | naming
Decide whether Pragmatic Data Vault is the right architecture for a given use case, and what to consider before starting
XTS-assisted satellite loading pattern for late-arriving and out-of-sequence data. Generates XTS DDL, the out-of-sequence SWITCH, and the timeline-correcting UNION ALL load for bi-temporal Data Vault satellites.