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The information delivered from the data warehouse helps the senior leaders make informed decisions based on facts. There are two main data warehousing methodologies upon which all other data warehousing methodologies are built. These two gentlemen see the data warehouse from two very different perspectives:. Only after all of the full collection of atomic data is collected from the source systems can the data marts for analytics and reporting be created.

What is a data warehouse?

These data marts are often customized for specific departments or user groups. This is a bottom-up methodology seeking to build small dimensional data marts of fact tables measures surrounded by dimension tables entities for specific departments of user groups. Each data mart provides the data for the analytics and reporting tools.

Kimball sees the data warehouse as the collection of all of the data marts in the organization.

Although there are two basic data warehousing methodologies many organizations create their own data warehousing methodology to best suit their organizational needs. I have created several data warehouses for many organizations in both the public and private sectors. I am hoping to use this blog site as a resource for those entering the field of data warehousing to learn the fundamentals of Data Warehousing as well as providing some tips and tricks for those interested in optimizing their data warehouse.

Fundamentals of Data Warehouses

I hope that you find this information informative. I will be posting a new article each Wednesday and I look forward to your feedback and discussion about the weekly topic. Please come back each Wednesday for more information about Data Warehousing and Business Intelligence. Not a techie…but do you think that concentration of any IT resources in one location increases the risk of bad actors compromising the security?

Get Trained by Industry Expert. Course Prerequisites Knowledge of general data warehousing concepts.

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DATA WAREHOUSING FUNDAMENTALS: A Comprehensive Guide for IT Professionals [Book]

Describe methods and tools for extracting, transforming and loading data. Identify some of the tools for accessing and analyzing warehouse data. Describe the benefits of partitioning, parallel operations, materialized views and query rewrite in a data warehouse. Explain the implementation and organizational issues surrounding a data warehouse project.

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