Confusion persists regarding the role of data warehouses, data lakes and data hubs. They are NOT the same. While the names and terminology are less important than the principles and capabilities, data and analytics leaders need to know the capabilities and the value they provide.
• What are data warehouses, data lakes and data hubs?
• What are the key differences between these concepts?
• How can they be used in combination to meet modern data and analytics needs?

The intense focus on data scientists has distracted organizations from an essential element of their analytics success — the delivery of quality data ready for analysis. This session explores the role of the data engineer and how it contrasts with the data scientist. Specifically, it covers:
• What do data engineers do, anyway?
• What are the necessary data engineering skills?
• Where do data engineers fit in your data and analytics program?

Data and analytics leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?
2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?
3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?
*Access to this session is restricted to those that are not part of an organization/company that provides services, products or solutions to professionals in the industry.