Analytics relies on successful data foundation; it must be backed with right data and processes. Data engineering is the discipline of translating data into usable forms. Specifically, this session covers:
1. Data Engineering, DataOps, Data Engineer: The Basics
2. Three Emerging Best Practices You Must Investigate
3. How To Formalize and Scale Data Engineering Practice

When technology boundaries covered new grounds, we gave them different names such as Data Catalog, Data Virtualization, Logical Data Warehouse, Knowledge Graph, Data Hub, Data Fabric. However, there is a great deal of overlapping capabilities and missing gaps. This session will raise awareness of various technology components and eventually enable D&A leaders build their digital enterprise.

Many organizations have created a data lake but find it difficult to use and maintain. In this roundtable we will discuss the issues around data lake and what can be done to realize greater value. The discussion will encompass: 1. What the issues are seen with a data lake; 2. How to design a lake and configure its infrastructure for technical success; and 3. How to gain more business value from the data lake. *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.

The new data and analytics platform is the cloud. In this session, we will explore the state of the data and analytics platform (data management, BI/analytics/data science, data and analytics governance), the transition to cloud and its implications, and what it means to operate successfully in the cloud, or more likely, in multiple clouds.