Data Warehouses and Enterprise Data Warehouses

Data Warehouses and Enterprise Data Warehouses

Data Warehouses and Enterprise Data Warehouses: what is the difference?

Since the 1990s Data Warehouses have attracted great attention. The original goal of a Data Warehouse is to collect data from the source systems servicing the execution of the business processes and from external data sources, and then to transform these into a structure suitable for reporting and analysis purposes. Such a Data Warehouse is an Enterprise Data Warehouse, where among others data reconciliation is secured and where on the (non) implementation of Reference & Master Data Management in the organization and on Data Quality issues occuring in the source datasets is anticipated; not solved, but that is another topic.

With the introduction of “Big Data” new IT Infra and tooling have been marketed by a number of Cloud Service Providers to develop and to deploy data warehouses. What is remarkable, when you read articles and blogs or watch posts on among other YouTube about these new IT Infra and tooling, is that starting points of Enterprise Data Warehouse design and development are undiscussed. Current examples show how a source dataset is collected and processed downstream - Extract, Load, Transform and Load ELT(L) - as part of data analytics and data science activities or as part of providing a result dataset to a system. Nowadays this concept is called a Data Warehouse.

The difference between a Data Warehouse and an Enterprise Data Warehouse is obvious: an Enterprise Data Warehouse is an integrated data solution (where accountancy principles are secured and represented by a BUS Matrix when the Dimensional Modeling methodology is applied), whereas a Data Warehouse is just a bunch of datasets.