Difference Between Enterprise Data Hub, Data Lake, and Data Warehouse

Data lakes (also see: Data Lake) are centralized repositories of unorganized structured, and unstructured data with no governance and specifications for organizational needs. The primary purpose of a data lake is to store data for later usage though many data lakes have developer tools that support mining the data for various forward-looking research projects.

A Data Warehouse organizes the stored data in a prescribed fashion for everyday operational uses, unlike a data lake. Data Warehouses can be multitiered to stage data, transform data and reconcile data for usage in data marts for various applications and consumers of the data. A data warehouse is not as optimized for transactional day-to-day business needs as an enterprise data hub.

In addition to drawing data from and pushing data to various enterprise applications, an Enterprise Data Hub can use a data lake, data warehouse, and other data sources as input into or as destinations from the data hub. Once all the data is available for the hub, the aforementioned features, such as governance, can be applied to the data. Enterprise data hub vs data lake can be easily differentiated based on the data hub’s additional capabilities for processing and enriching the enterprise data. Enterprise data hub vs data warehouse can be confusing, but the data hub has additional capabilities for using the data more business process-oriented rather than business analytics-oriented operations.

Enterprise Data Hub Architecture

The following diagram shows a data hub architecture that includes multiple data sources, the hub itself, and the data consumers.

The Enterprise data hub Architecture is designed for the most current needs of organizations. The architecture itself can grow to accommodate other data management needs, such as the usage of data in emerging technologies for decision support and business intelligence.