A schema refers to the structure or organization of a database. It contains a logical description of the entire database, which includes names and descriptions of tables, records, views, and…
Understanding Dimensional Modeling: The Basics of a Kimball Data Warehouse
Introducing the data warehouse and business intelligence industry to dimensional modeling in its current form in 1996, the Kimball Group has since published numerous articles and tips that cover dimensional…
An Overview of Logical Data Warehousing
Popularized by Gartner IT analyst Mark Beyer in 2011, the term “logical data warehousing” is defined as an architectural layer that combines the strength of a physical data warehouse with…
ETL vs. ELT: Transform First or Transform Later?
Starting off in the early 90s for data warehousing, large companies that ran substantial transactions and had huge user base used Extract-Transform-Load (ETL) processes to consolidate transactional data across all…
What is Data Virtualization?
At its core, data virtualization falls within the domain of data integration. But unlike traditional data integration where Extract-Transform-Load (ETL) processes are used to physically move copies of data from…
The Role of ETL in a Data Warehouse Architecture
Integrating, reorganizing, and consolidating large amounts of data from a variety of different sources is a key consideration when planning your data warehouse architecture. Extract-Transform-Load (ETL) processes are used to…
Tracking Historical Data in Your Data Warehouse Using Slowly Changing Dimensions
When building business intelligence applications using the enterprise data warehouse or production databases as the source, attribute changes need to be handled with considerable care. The way you model your…
A Guide to Data Modeling and its Different Phases
Data modeling refers to the exploration of data-oriented structures. It is a process that involves documenting a complex software design in a comprehensive diagram, using symbols and texts, to represent…