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…
4 Data Warehouse Optimization Mistakes to Avoid | Data Warehouse Info Center[…] true that analytics experts have used the snowflake and star schemas to get better vi...
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…
Star Schema vs. Snowflake Schema | Data Warehouse Information Center[…] two types of schemas in a dimensional data warehouse that we’ll discuss in this ar...
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…
Types of Data Integration | Data Warehouse Information Center[…] from different source systems. That’s why data virtualization layer is also referre...
Logical Architecture for Analytics | Data Warehouse Information Center[…] is not possible without significant data conversion and transformation, triggering th...
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…
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…
Data Warehouse Cleansing - Eliminate Bad Data in Your Data Warehouse[…] with the help of emerging data warehousing techniques like data warehouse automation ...
Date Virtualization for Agile Data Warehousing | Data Warehouse Info[…] data virtualization, the data rests at one place and is not moved or replicated to an...
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…
Implement Referential Integrity Constraints for Consistency & Error Control[…] you decide whether you should implement referential integrity in your data warehouse ...
The 3 Stages of Data Cleansing - Data Warehouse Information Center[…] the ubiquity of Extract-Transform-Load (ETL) processes for data integration, ETL tool...
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…
Advanced Dimensional Modeling Techniques – An Overview[…] Slowly changing dimension techniques […]...
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…
Testing for Agile Data Warehouse Environment | Data Warehouse Information Center[…] develop a test strategy, you need to understand data modeling, and map data from sour...
Understanding Dimensional Modeling| Data Warehouse Information Center[…] to change or extend data models to reshape them as business requirements change, givi...