The Rise of Logical Data Warehouse (LDW)

The very core of data management is rapidly evolving as the speed and volume of data is growing beyond what yesterday’s tools can handle. It seems that just last year, IT departments were initiating new physical Data Warehouse (DW) projects in an attempt to address the data needs of the business. These same companies are fortunate if they can finish out their infrastructure this year. Meanwhile, other IT departments started projects using Logical Data Warehouses (LDW) late last year. These Logical Data Warehouse initiatives already have significant numbers of BI users leveraging hundreds of query-able services today.

Logical Data Warehouse vs. Classic Data Warehouse

A Logical Data Warehouse (LDW) is very much like a classic Data Warehouse, except :

  • LDW is up to 90% faster to implement
  • No data is stored in LDW. Data resides at the source
  • No ETL/programming is required
  • Does not require significant infrastructure
  • No latency of data delivered
  • Each data set is accessed via a range of services, e.g., SOAP, REST, Odata, SharePoint, ADO.Net


Read the latest LDW Whitepaper

Data Virtualization (DV) enables Logical Data Warehouses

Stone Bond Technologies has been offering Enterprise Enabler® since long before DV had a name. As a matter of fact our CTO pioneered the concept of this technology. Not all Data Virtualization technology is equal. Only Enterprise Enabler includes all these features in a single IDE (integration Development environment).  Cloud, on-premise, hybrid, iPaaS

  • Incorporate and federate ALL data sources…. IoT, Mainframes, data streams, Big Data, Data Lakes, ERPs, databases, flat files, services. No source is left behind.
  • A single platform for configuring, testing, deploying, and monitoring the LDW components
  • Ability to use the federation logic to write data back to the sources, with end user security.
  • Configurable caching for cases where partial data is better refreshed periodically
  • Reuse logic and components for both Data Virtualization and ETL (which actually is necessary in some DV scenarios
  • Many performance tuning techniques
  • Full versioning, audit trails, lineage
  • Web portal for managed self-serve data for BI