Logical Data Warehouse

Improve time-to-market with a centralized data access layer.

Logical Data Warehouse with the
Data Virtuality Platform

The concept of Logical Data Warehouse (LDW) was coined by Gartner in 2009 – driven by the business side which needs to derive value from the data. The concept of the LDW came to life because the old ETL process could not deal with all the use cases and ever changing business requests. As an architectural layer on top of a traditional data warehouse and data sources, the Logical Data Warehouse enables access to multiple, diverse data sources through a single data access layer to the users to meet every analytical use case.

By combining the two technologies, data virtualization and automated ETL, the Data Virtuality Platform enables the Logical Data Warehouse architecture. All relational and non-relational data sources can be consolidated and used for immediate analysis with SQL.

Data Virtuality Platform
Data Virtuality Platform Architecture

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Your Key Benefits and Features

Bridged Data Silos and Faster Time-to-Market

All data sources, whether in the cloud or on-premises can be easily integrated in a single data access, delivery, and modeling layer with the Data Virtuality Platform. This way, data silos can be eliminated and avoided. Time-to-market can be improved by up to 5 times.

Powerful Data Management with Procedural SQL

The virtual layer of the Data Virtuality Platform provides a flexible way to integrate and orchestrate different systems using procedural SQL. The procedural SQL capabilities allow to manage even complex data logic and data transformation processes in just one place. Challenges like master data management (MDM), data cleansing, and data historization can be easily solved. Finally, data can be written back into the sources.

Improved Data Quality and Data Governance

The virtual layer allows defining rules to check the data quality in a uniform way using SQL. Even complex rules for checking data quality are made easier with procedural SQL. Furthermore, transparency, accountability and auditability can be ensured with the data lineage features.

Access Our Resources

Data Architectures for Data Science Using Data Virtualization

In this whitepaper, Rick van der Lans explains how a modern data architecture can help data scientists to be faster and to work more efficiently.

Data Fabric Q&A Paper with Forrester

In this Q&A paper Noel Yuhanna, VP and Principal Analyst at Forrester, answers frequently asked questions about the Data Fabric architecture.

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