Instead, the ETL layer connects with the data warehouse, where the cleansed data is held. Dependent data marts: Dependent marts do not directly interact with data sources.Independent data marts can act as a source for a data warehouse. Independent data mart: Raw data is extracted and transformed by ETL, and then loaded directly into the data mart.In practice, this allows organizations to structure their data marts in several ways: Transformation only needs to be performed one time – once it fits the desired schema, data can then directly be passed to the next repository. A data mart is usually owned by the relevant department.īoth structures rely on an ETL (Extract, Transform, Load) process to provide them with cleansed, normalized data. Ownership: Warehouses are owned by the organization’s data management team.Warehouses ingest data from multiple sources across the organization’s infrastructure. Sources: Marts use a limited number of sources, such as a data warehouse or departmental databases.Line of business: Data marts support a single department while a data warehouse supports the entire organization.Size: Data marts are generally under 100 GB, while warehouses have no fixed size limit.However, the two have some crucial differences: Both are relational databases, both work on production data that has been transformed, and both are used for analytics purposes. Relationship Between a Data Mart and a Data Warehouseĭata marts and data warehouses have much in common. Marts can be dependent on warehouses, or they can acquire raw data from other sources. A data mart is a repository that holds data relevant to a group of users with common needs, such as a business department.ĭata marts generally exist in relation to a data warehouse.
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