data_vault_2

Data Vault 2.0 modeling requires constructing hubs, links, satellites, and reference structures, plus performance helpers like PIT and bridge tables. You can generate the full modeling blueprint, including staging views with hash keys and DDL for insert-only loads.

Hash keys and hash diffs in Data Vault 2.0 require deterministic generation using normalization, compound key delimiters, and NULL sentinels. This ensures consistent joins across integration points and reliable change detection for insert-only loading patterns.

Multi-active and effectivity satellites are specialized Data Vault satellite variants for tracking concurrent records and temporal validity. They require specific insert-only DML patterns to correctly manage overlapping dates and multiple active states without destructive updates.

Data Vault 2.0 modeling applies across SQL, dbt, Spark, and Airflow-style workflows for data engineering pipelines. It supports both batch and streaming ingestion to build raw vault schemas, business vault computed attributes, and information mart views.

Information mart translation involves applying business rules via computed satellites in the Business Vault, then constructing analyst-friendly dimensions and facts. This process builds queryable views directly on top of the underlying vault structures.

PIT and bridge tables are performance helpers needed in Data Vault 2.0 when querying point-in-time historical states or joining multiple satellites across different time periods. They optimize query performance by pre-calculating complex temporal joins.