The data warehouse diagram series
These diagrams show how data is processed by the Quantemplate platform and in a classic and virtual data warehouse
Classic data warehouse
In a classic DWH data is copied periodically from production systems to the central data warehouse – usually a relational database – with a pre-defined schema. Sometimes before data is moved into the central data warehouse it is first stored in an operational data store (ODS).
CDW was the best solution for databases until early 2000s. The state of Extract-Transform-Load (ETL) and of reporting did not really allow for the development of other more sophisticated DW technologies. All ETL and Business Intelligence (BI) tools were aimed at supporting this CDW technology.
Virtual data warehouse
A data management architecture for analytics which combines the strengths of traditional repository warehouses with alternative data management and access strategies.
Virtual Data Warehousing is the technology that offers data consumers a single view for querying and manipulating data stored in a heterogeneous set of data stores – both traditional structured data sources and less structured sources such as Hadoop, NoSQL – all while still appearing as a single ‘logical’ data source to the data consumer.
The Quantemplate platform
The Quantemplate platform is a proprietary technology that combines the best-of-breed computer science approaches for each step of the data lifecycle.
The goal is no different to what anyone else is trying to accomplish – bring together data from disparate data sources into one place, run queries against that, and share the findings. The data warehousing infrastructure has similar capabilities to virtual (or logical) data warehouse infrastructure. This technology solves the problem of inflexibility that is inherent in a classic enterprise data warehouse.