Data Architectures for Data Science Using Data Virtualization

Data Architectures for Data Science Using Data Virtualization


Data scientists are confronted with some major challenges: a fast-changing data storage technology landscape, new restrictive regulations for data privacy, and time-consuming data preparation tasks. In fact, studies have shown that data scientists spend only 20% of their time on real analytical work and as much as 80% of their time on data preparation tasks. In this webinar, we look at how a modern data architecture can help data scientists to be faster and to work more efficiently.

The on-demand webinar is structured in three parts:

  1. Detailed explanation of the major challenges that are the reason why data scientists spend so much time on searching, accessing, and querying the data.
  2. Will cloud platforms and data lake solutions possibly solve these challenges?
  3. How data virtualization features can speed up the work of data scientists, and how it helps to deal with all the challenges. More specifically, a flexible data architecture, called the logical data lake, is described in which data virtualization acts as the general entry point for data scientists to access data.

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