ETL Processes. To bring data from the transaction system, a process called ETL is used. ETL stands for extract, transform and load. ETL process consolidates data, transform it into a specific standard format and load it into a single repository called enterprise data warehouse, or EDW. ETL processes can run as a batch process periodically or a transaction-based for near real-time data.
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ETL Processes. To bring data from the transaction system, a process called ETL is used. ETL stands for extract, transform and load. ETL process consolidates data, transform it into a specific standard format and load it into a single repository called enterprise data warehouse, or EDW. ETL processes can run as a batch process periodically or a transaction-based for near real-time data. ETL process is referred as data integration or data services. Data in the enterprise data warehouse is captured at a very lowest level of detail.
Data in the enterprise data warehouse is stored in relational database and uses third normal database design. Data marts are departmental views of information with subject-oriented data. Data marts take data from enterprise data warehouse. Aggregations can take place when data brings from enterprise data warehouse to data marts. Data marts use dimensional design, therefore, the data in the data marts is ready for analysis.
It is important to note that all the external applications or reporting tools or business intelligence tools query data from data marts instead of enterprise data warehouse directly. In Bill Inmon data warehouse architecture, data is organized using ER modeling.
Enterprise data warehouse is the hub that provides data for data marts. Analytic systems query data against data marts not directly from the enterprise data warehouse. Was this tutorial helpful?
Bill Inmon vs. Ralph Kimball
Inmon Data Warehouse Architectures Kimball vs. Inmon Data Warehouse Architectures Summary: in this article, we will discuss the differences between Kimball and Inmon in data warehouse architecture approach. Both architectures have an enterprise focus that supports information analysis across the organization. This approach enables to address the business requirements not only within a subject area but also across subject areas. However, there are some differences in the data warehouse architectures of both experts: Kimball uses the dimensional model such as star schemas or snowflakes to organize the data in dimensional data warehouse while Inmon uses ER model in enterprise data warehouse. Inmon only uses dimensional model for data marts only while Kimball uses it for all data Inmon uses data marts as physical separation from enterprise data warehouse and they are built for departmental uses. In dimensional data warehouse of Kimball, analytic systems can access data directly.
Kimball vs. Inmon Data Warehouse Architectures
Data Warehouse Design — Inmon versus Kimball Data Warehouse Design — Inmon versus Kimball Published: Author Sakthi Rangarajan Introduction We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively. The data warehouse, due to its unique proposition as the integrated enterprise repository of data, is playing an even more important role in this situation. There are two prominent architecture styles practiced today to build a data warehouse: the Inmon architecture and the Kimball architecture. This paper attempts to compare and contrast the pros and cons of each architecture style and to recommend which style to pursue based on certain factors. Background In terms of how to architect the data warehouse, there are two distinctive schools of thought: the Inmon method and Kimball method.