2016/12/08 - Westin Galleria Dallas, Dallas, Texas
9:00AM to 1:00PM CT
Speakers
Self-service BI, Logical Data Warehouse and Data Lakes – They are all essential components of Fast Data Strategy. Many companies are rapidly augmenting their traditional data warehouses, data marts, and ETL with their logical counterparts. Reason? Agility and rapid time-to-market.
Attend this half-day seminar and learn from the data experts:
- Why self-service BI powered by logical data warehouse and data lakes built using big data are the future of fast data strategy
- The correct approaches to such modern data architectures and reaping high performance benefits
- Hear directly from a customer who successfully implemented these solutions
- Deep dive into the use cases with product demos.
Additionally, network with your peers as well as customers who have successfully implemented these solutions at lunch, and during the break. Best of all – the entire event is free.
| TIME | SESSIONS |
| 09:00 – 09:45am | Customer Use Case: Powering Self-Service BI with Logical Data Warehouse and Operationalizing Logical Data Lakes Chuck DeVries, VP, Strategic Technology and Enterprise Architecture, Vizient |
| 09:45 – 10:15am | Logical Data Lakes/ Warehouse: Architectural Patterns and Performance Considerations Ravi Shankar, Chief Marketing Officer, Denodo |
| 10:15 – 10:45am | Demo: Building Logical Data Lakes/ Warehouse using Data Virtualization Chris Walters, Sr. Solutions Consultant, Denodo |
| 10:45 – 11:00am | Networking Break |
| 11:00 – 11:30am | Best Practices: Big Data Virtualization Deployment and Management Charles Yorek, Vice President iOLAP |
| 11:30am – 12:00pm | Panel: Self-service BI, Logical Data Warehouse, Data Lakes Chuck DeVries, VP, Strategic Technology and Enterprise Architecture, Vizient Chris Walters, Sr. Solutions Consultant, Denodo Speaker, iOLAP Moderator Ravi Shankar, Chief Marketing Officer, Denodo |
| 12:00 – 01:00pm | Networking Lunch |
Logical Data Warehouse
A Logical Data Warehouse is a data system that follows the ideas of traditional Enterprise Data Warehouse (star or snowflake schemas) and includes, in addition to one (or more) core data warehouses, data from external sources. The main motivations are improved decision making and/or cost reduction.
Logical Data Lakes
A Logical Data Lake will not have a star or snowflake schema, but rather a more heterogeneous collection of views with raw data from heterogeneous sources. The virtual layer will act as a common umbrella under which these different sources are presented to the end user as a single system. However, from the virtualization perspective, a Logical Data Lake shares many technical aspects with a Logical Data Warehouse.