![]() Data Warehouses evolved in many ways, so that today it is more than just a database delivering data in a performant way. What we see is that a Data Warehouse is a central data platform consolidating structured data from different domains to deliver an harmonized and integrated view of data for historical data analysis. Picture 1: Generalized view of a Data Warehouse and it’s advantages To simplify the discussion I drawed the following overview: Data architectures are getting more and more complex and it can be helpful to concentrate on the basics to better understand the value they deliver for enterprises and business users.Īs the Data Warehouse is a concept with more than 30 years of history, it is still evolving and especially the cloud DWH providers like Snowflake, Databricks or the hyperscalers show dynamic innovations and many further software services like dbt or LookML are available to build modern Data Warehouses. ![]() The Data Warehouse is a design pattern for managing enterprise data. Furthermore around these basic patterns we have tools and solutions enhancing this architectures like Data Catalogs, Data Integration and Transformation, Data Governance and a lot more. These are the basic design patterns which can be combined, multiplied and adapted in different ways. Data Mesh (recommend reading Wolfgang Eptings blogs about).Today we find a lot of different data architectures available like: Often I see confusion because people in IT and business discuss about tools and projects, but not having a common understanding about what the data architecture in place is used for.
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