The Logical Data Warehouse: What it is and why you need it
TDWI Speaker: Philip Russom, TDWI Research Director
Date: Wednesday, June 24, 2015
Time: 9:00 a.m. PT, 12:00 p.m. ET
A logical data warehouse is an architectural layer that sits atop the usual data warehouse (DW) store of persisted data. The logical layer provides (among other things) several mechanisms for viewing data in the warehouse store and elsewhere across an enterprise without relocating and transforming data ahead of view time. These views also serve as interfaces into disparate data and its sources. In other words, the logical data warehouse complements the traditional core warehouse (and its primary function of a priori data aggregation, transformation, and persistence) with functions that fetch and transform data, in real time (or near to it), thereby instantiating non-persisted data structures, as needed.
The advantage of the logical layer is that data is fresher (as required by time-sensitive business processes) and the structure of delivered data is created at run time (as required by discovery oriented analytics), without limiting data to the pre-built structures of the DW’s persisted store. Achieving these advantages has been a challenge in the past, because software, hardware, and networks simply lacked the speed, scale, and reliability required of large, complex, ad hoc instantiations. Today, multiple advances have made the logical data warehouse fully practical, such that it’s time for more organizations to embrace it.
In this Webinar, you’ll learn:
- What a logical data warehouse is and does
- Common manifestations of logical data warehouses
- Related concepts, such as virtual data warehouses, data lakes, and data vaults
- Business-driven use cases for logical data warehouses
- Enabling technologies and practices that make the logical data warehouse fully practical today, including semantic layers, data view technologies, data fabric, real-time functions, high-performance data warehousing, columnar databases, and in-memory databases and analytic processing
Philip Russom, Ph.D.