A Data Warehouse (DWH) connects business data from different sources. A Data Warehouse collects, consolidates and transforms data from different sources to generate key business insights and is an essential part of the BI system, providing the basis for data analysis and reporting.
A Data Warehouse makes it easier for a decision maker to analyze data and share this information with colleagues. Managers can set data-driven strategies and make decisions based on the Facts.
A Data Warehouse provides insight into the historical events of the organization to evaluate past initiatives. With this information, managers can see where they need to adjust their strategies to reduce costs and increase productivity and revenue.
Traditional Data Warehouses have been implemented locally (on-premise). But with the advent of cloud platforms like Azure and Amazon, companies no longer need to invest in expensive hardware and IT staff. In addition, cloud platforms have the ability to deliver dynamic capacity and high performances which makes the solution very interesting.
Data Marts usually are subsets of the Data Warehouse that contains data for specific business functions. These subsets are mainly set up due to the capacity required to process large data sets. Recent cloud developments, such as the column-based storage platforms Amazon Redshift and Snowflake, makes direct integration of web and business data in Data Marts quite efficient.
The development of a data warehouse is often a time-consuming process. By intelligently using the right metadata, recurring ETL (Extract, Transform & Load) patterns can be automated and the implementation time of a data warehouse can be significantly reduced.
So, that's it for today! Although the concept of data warehouses is still quite new to many organizations, it is constantly evolving and reaching new heights. Throughout the article we have seen what a Data Warehouse is, why they are used and some developments.