Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment
Attend this session to learn:
- Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
- Why a multi-purpose data lake is essential in building a universal data delivery system.
- How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.