Data federation and data virtualization are both terms that have been coined to describe the process of collecting and compiling data. Data virtualization is a superset of the ten-year old data federation technology. It also includes the advanced capabilities of performance optimization as well as self-service search and discovery. Data federation is a distributed access to data residing in multiple systems with the purpose of joining the data together as if it came from the same system. Data virtualization on the other hand, has evolved from data federation by improving performance and adding other advanced capabilities such as self-service search and discovery. Advanced data virtualization products like the Denodo Platform have evolved to include dynamic query optimization techniques to determine the best query execution plan thus delivering optimal performance times.  Read more at: http://www.datavirtualizationblog.com/leading-analysts-mixing-data-federation-data-virtualization-terms/

Data Lake helps data scientists by reducing their time taken to gather data and start their real work of data analysis. Copying data physically to one centralized environment may be problematic because storing big data can be costly, copying of data can be prohibited. Metadata describing the data is commonly not copied along with the data and therefore not available to the data scientists. Also, technical and organizational management of a data lake is required. Since data scientists ask for easy and quick data access, a more practical solution to it is a logical data lake. A logical data lake, hides where the data is physically stored and whether it has been copied or not. Logical data lakes can be developed with data virtualization servers such as the Denodo Platform. While, copying and physically storing the data twice is the default approach for the physical data lake, it's optional for the logical data lake. It offers access to data without copying if required and to copy data when needed.  Thus, logical data lake is a better solution for data scientists. Read more at: http://www.datavirtualizationblog.com/data-scientists-physical-data-lakes/