Data Warehouse Architecture

Data Warehouse Architecture

 Data Warehouse Architecture refers to the structured framework used to collect, integrate, store, and manage large volumes of data from multiple sources for analytical and reporting purposes. It provides a centralized repository that supports business intelligence, data analytics, and strategic decision-making. Traditional architectures often follow a three-tier model—data source layer, staging/ETL layer, and presentation layer—while modern architectures integrate cloud platforms and scalable storage solutions. Foundational concepts were introduced by pioneers such as Bill Inmon, who defined the data warehouse as subject-oriented, integrated, time-variant, and non-volatile, and Ralph Kimball, who emphasized dimensional modeling and data marts. Modern implementations frequently leverage platforms like Amazon Redshift, Google BigQuery, and Snowflake to enable scalable, cloud-based analytics. A well-designed data warehouse architecture ensures data consistency, high performance, scalability, governance, and secure access for enterprise-wide decision support.

Data Warehouse Architecture, ETL (Extract Transform Load), ELT, Data Integration, Dimensional Modeling, Fact Tables, Dimension Tables, Data Marts, OLAP (Online Analytical Processing), Star Schema, Snowflake Schema, Metadata Management, Data Governance, Cloud Data Warehouse, Enterprise Data Warehouse (EDW), Data Staging Area, Business Intelligence (BI), Data Modeling, Distributed Storage

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