For their machine learning and data science projects to be successful, data scientists need access to all of the enterprise data delivered through their myriad of data models. However, gaining access to all data, integrated into a central repository has been a challenge. Often 80% of the project time is spent on these tasks. But, a virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data science ecosystem. There is no need to change tools and learn new languages. The data virtualization platform helps data scientists offload these data integration tasks, allowing them to focus on advanced analytics.
In this session, you will learn how data virtualization:
- Provides all of the enterprise data, in real-time, and without replication
- Enables data scientists to create and share multiple logical models using simple drag and drop
- Provides a catalog of all business definitions, lineage, and relationships