The classic data warehouse architecture has served many organizations well over the last twenty years, but it’s not the right architecture for this new world of BI. It’s time for organizations to migrate gradually to a more flexible architecture: the logical data warehouse architecture. This architecture, introduced by Gartner, is based on decoupling reporting and analyses from data sources.
Classic data warehouse architectures are made up of a chain of databases. This chain consists of numerous databases, such as the staging area, the central data warehouse and several data marts, and countless ETL programs needed to pump data through the chain. Integrating self-service BI products with this architecture is not easy, especially not if users want to access the source systems. Delivering 100 percent up-to-date data to support operational BI is difficult to implement. Plus, how do we embed new storage technologies into this architecture?
With the logical data warehouse architecture, new data sources can be hooked up to the data warehouse more quickly, self-service BI can be supported correctly, operational BI is easy to implement, the adoption of new technology is much easier, and the processing of big data is not a technological revolution, but an evolution.
The technology to create a logical data warehouse is available, and many organizations have already completed the migration successfully—a migration based on a step-by-step process, not a full rip-and-replace.
In this practical seminar, the architecture is explained in detail. It discusses how organizations can migrate their existing architecture to this new one. Tips and design guidelines are provided to help make this migration as efficient as possible.
You Will Learn
- The benefits of the logical data warehouse architecture and how it differs from classic data warehouse architecture
- How easily new data sources can be made available for analytics and data science
- Short overview of data virtualization technology.
- How self-service analytics can be supported by a logical data warehouse, and how it helps to share specifications across different analytics tools.
- How your organization can successfully migrate to a flexible logical data warehouse architecture in a step-by-step fashion
- How access to big data stored in Hadoop and NoSQL systems can be made available to analysts easily and transparently
- The differences between a physical data lake and a logical data lake
- How LDWs help integrate self-service analytics with classic forms of business intelligence
- The real-life experiences of organizations that have implemented a logical data warehouse
- Upgrading from a logical data warehouse to a unified data delivery platform.
- The relationship between the unified data delivery platform and the data fabric.
- Business intelligence specialists; data analysts; data warehouse designers; business analysts; data scientists; technology planners; technical architects; enterprise architects; IT consultants; IT strategists; systems analysts; database developers; database administrators; solutions architects; data architects; IT managers