Data lakes and logical data warehouses are both very popular topics in the world of business intelligence—data lakes for big data and data science and data warehouses for the development of agile BI systems. However, do users really need the data to be physically stored in one environment, as in a data lake, or is the need actually to be able to access all that data in its raw format when they need it?
As you can see, it’s not about storage. It’s about fast and easy access! With a logical data lake (or virtual data lake), data stays where it was produced and is presented as one logical environment when it’s queried—fast and easy. This avoids complications with network bandwidth, company politics, data protection and privacy, refresh issues, and so on. Similarly, with a logical data warehouse, the data appears to be in a single database, although it isn’t stored that way. Therefore, it makes sense to combine these two environments.
This tutorial explains in detail what a logical data lake and what a logical data warehouse architecture is, and it will discuss how the two can been combined and how they can benefit from each other.
You Will Learn
- What a data lake is and why organizations would want one
- What a logical data warehouse architecture is and its design guidelines
- The practical limitations of a physical data lake
- How to develop a logical data lake using data virtualization technology
- Caching virtual tables to speed up queries on the data lake
- Integrating the logical data lake and the logical data warehouse architectures
BI specialists and DW designers looking to learn the pros and cons of the logical data lake and logical data warehouse; data scientists, data analysts, and business analysts; technology planners and architects; database developers and administrators; IT managers who need to be informed about what the logical data warehouse architecture has as business benefits