Prerequisite: None
John O'Brien
President
Radiant Advisors
With over 25 years of experience delivering value through data warehousing and BI programs, John O’Brien's unique perspective comes from the combination of his roles as a practitioner, consultant, and vendor CTO in the BI industry. As a recognized thought leader in BI, John has been publishing articles and presenting at conferences in North America and Europe for the past 10 years. His knowledge in designing, building, and growing enterprise BI systems and teams brings real-world insights to each role and phase within a BI program. Today, John provides research, strategic advisory services, and mentoring that guide companies in meeting the demands of next-generation information management, architecture, and emerging technologies.
When does a data lake make a good data warehouse or vice versa? In this course, John O’Brien will explore how the data lakehouse came to be and explain how to evaluate this latest pattern in modern architecture for your data strategy and architecture roadmap.
The data lakehouse leverages a cloud-native architecture with open data standards. It evolves how we approach enterprise data management to meet the growing demands in enterprise analytics and data management. There are many factors to consider whether the data lakehouse is the “open data architecture” approach to consider adopting. Each company’s journey must factor in its vendor ecosystems, open source projects, technology maturity, and own data strategy.
Attend this course to hear the data lakehouse story and learn how it may fit into your data and analytics architecture. O’Brien will also discuss prominent players, positions, and predictions for the coming years.
You Will Learn
- The data lakehouse principles and components
- Benefits of having a data lakehouse
- Challenges with a data lakehouse architecture
- How to evaluate your data architecture needs
- Data lakehouse technologies, vendors, and open source options
Geared To
- Data architects
- Cloud architects
- Data scientists
- Data engineers
- DW/BI teams
- Data and analytics leaders