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Data Mesh Webinar

Free On-Demand Webinar

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.

Rick van der Lans

CEO & Founder, R20/Consultancy

Nick Golovin

CEO & Founder,
Data Virtuality

The 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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Highlights From Our Resources

Data Architectures for Data Science Using Data Virtualization

Data Architectures for Data Science Using Data Virtualization

In this whitepaper, Rick van der Lans explains how a modern data architecture can help data scientists to be faster and to work more efficiently.

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Hybrid- and Multi-Cloud Architectures

Best Practices for Hybrid- and Multi-Cloud Architectures

This whitepaper describes best practices for orchestrating Hybrid- and Multi-Cloud architectures and how Data Virtuality can enable these.

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