An enterprise uses an average of 928 applications, along with many other on-premise systems. This means that you can have about a thousand source systems from different vendors, each storing…
Date Virtualization for Agile Data Warehousing | Data Warehouse Info[…] Data virtualization serves as an alternative to ETL and traditional data warehousing ...
Data is getting bigger every second, with research firms predicting that each person will generate at least 1.7 megabytes of data every second by 2020. So it’s no surprise that…
After the initial phases of gathering requirements and conceptualizing data warehouse design, the execution phase begins. Extract-Transform-Load from source data systems to destination staging database or data warehouse. The process…
Testing for Agile Data Warehouse Environment | Data Warehouse Information Center[…] testing begins by identifying source data. The identified and extracted source data i...
Role of Data Warehouse Components | Data Warehouse Information Center[…] Methods of extraction […]...
Too many buzzwords, too little understanding. With the ubiquity of data and analytics, IT vocabulary is expanding fast, and terms like data marts, data lakes and data warehouses are being…
Data Warehousing Trends - Data Warehouse Information Center[…] recent developments in the construction of data marts allow integration of web and en...
Business intelligence and database teams have a range of options when conceptualizing their data warehouse design. You could go with either the Kimball or Inmon route and decide whether to…
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...