A data vault architecture is a data modeling approach that is designed to provide long-term scalability, flexibility, and agility in data warehousing and business intelligence environments. It consists of a set of principles and practices that enable the construction of a highly adaptable and scalable data warehouse.

Here are the key components and characteristics of a typical data vault architecture:

1. Hubs: Hubs represent the central business entities or concepts in the data vault. They serve as the primary integration points for data from various sources. Each hub contains a unique list of business keys and is associated with satellite tables.
2. Satellites: Satellites are tables that store detailed attributes related to the hubs. They contain descriptive information about the business entities, such as their historical changes, timestamps, sources, and other relevant metadata. Satellites are linked to their corresponding hubs through foreign keys.
3. Links: Links establish relationships between hubs. They capture the associations or interactions between different business entities. Links are often used to represent complex many-to-many relationships. Like hubs, links can have associated satellite tables to store additional attributes.
4. Vaults: Vaults serve as a container for hubs, satellites, and links. They provide a logical grouping of related data entities. Vaults are typically designed to be independent and self-contained, allowing for easier management and scalability of the data warehouse.
5. Historical Tracking: A key feature of data vault is its ability to track historical changes in the data. Satellites store historical snapshots of the attributes, enabling analysis of how the data has evolved over time. This historical tracking capability provides a reliable audit trail and supports temporal analysis.
6. Flexibility and Scalability: Data vault architecture is designed to be highly flexible and scalable. New data sources can be easily integrated by adding new hubs, satellites, and links without requiring significant modifications to the existing structure. This adaptability is particularly beneficial in dynamic environments where new data sources and business requirements emerge frequently.
7. Automation and Data Lineage: Data vault implementations often leverage automation tools and processes to accelerate development and maintenance. Automation helps ensure consistency, reduces manual effort, and improves data lineage tracking, which is essential for data governance and compliance.
8. Agile Methodology: Data vault architecture aligns well with agile development methodologies. Its modular and iterative approach allows for incremental development, rapid prototyping, and quick response to changing business needs.

It's worth noting that while the above elements represent the core aspects of a data vault architecture, there can be variations and extensions depending on specific implementation choices and organizational requirements.