The Logical Data Warehouse: Design, Architecture, and Technology
Business Intelligence has changed dramatically the last years. The time-to-market for new reports and analysis has to be shortened, new data sources have to be made available to business users more quickly, self-service BI and Data Science must be supported, more and more users want to work with zero-latency data, adoption of new technologies, such as Hadoop, Spark, and NoSQL, must be easy, and analysis of streaming data and Big Data is required.
The classic Data Warehouse architecture has served many organizations well. 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 a decoupling of reporting and analyses on the one hand, and data sources on the other hand.
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 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 into this architecture?
With the Logical Data Warehouse architecture new data sources can 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 in which 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 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