{"id":3665,"date":"2023-03-25T10:36:59","date_gmt":"2023-03-25T09:36:59","guid":{"rendered":"https:\/\/www.architecturemaker.com\/?p=3665"},"modified":"2023-03-25T10:36:59","modified_gmt":"2023-03-25T09:36:59","slug":"what-is-data-vault-architecture","status":"publish","type":"post","link":"https:\/\/www.architecturemaker.com\/what-is-data-vault-architecture\/","title":{"rendered":"What is data vault architecture?"},"content":{"rendered":"

Data Vault architecture is a type of data warehousing that uses a hub and spoke model to store data. The hub is a central repository for all data, and the spokes are used to store data from specific sources. This type of architecture allows for flexibility and scalability, and is well suited for data warehouses that need to support multiple data sources.<\/p>\n

The data vault architecture is a type of data warehouse that is designed to provide a centralized location for all of an organization’s data. This architecture is often used by organizations that have a large amount of data that needs to be managed and accessed by a variety of different users.<\/p>\n

What is meant by Data Vault? <\/h2>\n

A data vault is a data modeling design pattern used to build a data warehouse for enterprise-scale analytics. The data vault has three types of entities: hubs, links, and satellites. <\/p>\n

Hubs are central tables that contain the key information about an enterprise. Hubs are typically used to track information about people, places, and things. <\/p>\n

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Links are tables that connect hubs to each other. Links contain the key information that connects two hubs. For example, a link table might contain information about the relationship between a person and a place. <\/p>\n

Satellites are tables that contain information about a specific hub. For example, a satellite table might contain information about a person’s address or a place’s latitude and longitude.<\/p>\n

The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema. The Data Vault provides a flexible foundation for data warehousing, data marts, and operational reporting.<\/p>\n

What is the difference between Data Vault and data warehouse <\/h3>\n

The data vault model is a more agile approach to data warehousing that relies on on-demand data transformation and department-specific data marts. This model allows for raw data to be stored as-is without applying business rules, making it easier to adapt to changing business needs.<\/p>\n

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Data Lakes and Data Vaults are both ways of storing data, but they have different purposes. A Data Lake is a storage for any kind of data in an untransformed state, while a Data Vault is a data integration architecture that stores historical data from multiple operational systems.<\/p>\n

What is the benefit of Data Vault? <\/h2>\n

Data Vault enables quicker data loading simply because a number of tables can be loaded at the same time in parallel. The model decreases dependencies between tables during the load process and simplifies the ingestion process by leveraging inserts only, which load quicker than upserts or merges.<\/p>\n

Data Vault 20 is a hybrid of 3NF and Dimensional (Star Schema) data models and is useful to overcome the drawbacks in the other models.<\/p>\n

The Data Vault 20 model is a combination of the 3NF and Dimensional data models, which overcomes the limitations of each approach.<\/p>\n

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3NF is a normalization technique that reduces data redundancy and improves data integrity. However, it can result in complex query patterns and require extensive data cleansing.<\/p>\n

Dimensional models are easy to query and provide good performance, but can suffer from data duplication and require significant upfront design effort.<\/p>\n

Data Vault 20 combines the best of both approaches, providing a flexible model that is easy to query and maintain.<\/p>\n

How do you implement Data Vault? <\/h2>\n

We always split by the source table and source system in order to keep our data organized. We Split by the rate of change:Attributes with different rates of change should potentially be stored in different satellites so that we can keep track of them easily. Finally, we Split by data classification: depending on the degree of importance or security of the data, we may want to keep it in a different location.<\/p>\n

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The Snowflake Data Cloud is a cloud-based data storage and processing platform that enables users to quickly and easily access all their data assets at any scale. The Data Vault 20 approach is a data management methodology that enables users to create a centralized repository of all their data assets, which can be accessed and used by any team member regardless of their location or device. The combination of these two technologies is allowing teams to democratize access to all their data assets, which is helping to drive better decision-making and faster innovation.<\/p>\n

What problem does Data Vault solve <\/h3>\n

The data vault approach to data management is concerned with maintaining a record of the business keys that identify a particular entity, and the associations between those keys, separate from the descriptive attributes of the entity. This is intended to deal with the problem of change in the environment, by keeping the business keys that are relatively constant, and only updating the associations as needed.<\/p>\n

Data Vault is a modelling approach that is designed specifically for data warehouses. It is based on the principles of third normal form (3NF) but is optimised for data warehouses. Data Vault is Distinguished from other modelling approaches such as Star Schema (Dimensional modelling) and Third Normal Form (3NF) modelling.