Guidelines for designing a Logical Data Warehouse
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 datamarts, and countless ETL programs needed to pump data through the chain. This architecture has served many organizations well. But is it still adequate for all the new user requirements and can new technology be use optimally for data analysis and storage? Integrating self-service BI products with this architecture is not easy and certainly not if users want to access the source systems. Delivering 100% up-to-date data to support operational BI is difficult to implement. And how do we embed new storage technologies, such as Hadoop and NoSQL, into the architecture? It is time to migrate gradually to a more flexible architecture in which new data sources can hooked up to the data warehouse more quickly, in which self-service BI can be supported correctly, in which OBI is easy to implement, in which the adoption of new technology, such as Hadoop and NoSQL, is easy, and in which the processing of Big Data is not a technological revolution, but an evolution. The architecture that fulfills all these needs is called the Logical Data Warehouse Architecture. This architecture, introduced by Gartner, is based on a decoupling of reporting and analyses on the one hand, and data sources on the other hand. The technology to create a logical Data Warehouse is available, and many organizations have already successfully completed the migration; a migration that is based on a step-by-step process and not on full rip-and-replace approach. In this practical seminar, the architecture is explained and products will be discussed. It discusses how organizations can migrate their existing architecture to this new one. Tips and design guidelines are given to help make this migration as efficient as possible.
What you will learn
- What are the practical benefits of the Logical Data Warehouse Architecture and what are the differences with the classical architecture
- How can organizations step-by-step and successfully migrate to this flexible Logical Data Warehouse Architecture?
- You will learn about the possibilities and limitations of the various available products
- How do data virtualization products work?
- How can big data be added transparently to the existing BI environment?
- How can self-service BI be integrated with the classical forms of BI?
- How can users be granted access to 100% up-to-date data without disrupting the operational systems?
- What are the real-life experiences of organizations that have already implemented a Logical Data Warehouse?
- Challenges for the Classic Data Warehouse
- The Logical Data Warehouse
- Implementing a Logical Data Warehouse with data virtualization servers
- Improving the query performance of data virtualization servers
- Migrating to a Logical Data Warehouse
- Self-Service BI and the Logical Data Warehouse
- Big Data and the Logical Data Warehouse
- Physical data lakes or virtual data lakes?
- Implementing Operational BI with a Logical Data Warehouse
- The logical data warehouse and data vault
- The Logical Data Warehouse and the Environment