The logical data warehouse works by intelligently marrying two distinct technologies to create an entirely new manner of integrating data. The first technology is data federation, which connects two or more disparate databases and makes them all appear as if they were a single database. The second is analytical database management providing semantic business-friendly data element naming and modeling allowing flexible ingestion and modeling options.
The Functionality of a Logical Data Warehouse
The results are profound. Data federation alone offers flexibility, but can’t scale. Analytical database management scales beautifully, but is inflexible. The combination of the two enables breakaway flexibility and performance and represents an entirely new paradigm in the way we think, manage, and work with data.
For example, a logical data warehouse can connect to a variety of data sources simultaneously, including classic relational databases like Oracle and MS-SQL; No-SQL databases like MongoDB or Hadoop; column stores like Vertica or SAP HANA; or web services like Google Analytics, AdWords, Facebook, Twitter, and others. Once these have been connected, the resulting integrated overarching view of the data appears within a data analysis tool as if everything was contained in a single SQL database, accessible with a common query language. Virtually any data analysis tool currently on the market (such as Qlik, Tableau, Aqua Data, etc.) can connect, query, and analyze data over the virtual layer with no need to pull or copy data from any location.
Opportunities with a Logical Data Warehouse
The method offers vast new opportunities and possibilities for data exploration, data discovery, rapid prototyping, and intuitive experimentation. Business users can get results instantly and can refactor data models just as quickly. Further, building logical data views as shareable components including common KPIs and metrics can ensure that every report, every visualization, and every query response conforms to the same corporate standards and definitions. Data Virtuality acts as a central hub connecting all systems and applications within the enterprise, enabling data exchange between systems, and ensuring the latest data is available anywhere and anytime.
Advantages and Disadvantages of a Logical Data Warehouse
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