Presented at BIGIT 2020 (Malaysia)
For their machine learning and data science projects to be successful, data scientists need access to data from across the enterprise. However, gaining access to all of the data in an integrated central repository has been a challenge, resulting in up to 80% of the project time being spent on data acquisition and preparation tasks.
Data Virtualization can help the data scientist accelerate some of the most tedious tasks, including data exploration, acquisition and analysis, with the platform offloading these data integration tasks, allowing data scientists to focus on advanced analytics. As the Data Virtualization platform integrates well into the data science ecosystem, an added benefit is that the data scientist does not need to change tools or learn new languages in order to leverage all of the enterprise data.
In this on-demand 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
- Delivers a catalog of definitions, lineage, and relationships