Michael Gonzales

Urgent investments in AI-ready data and operational use cases have put the spotlight on foundational data management. The Data Fabric has emerged as a long-term data management architecture that you should now pursue for sustained data, analytics, and AI success. This session will help participants understand what data fabrics are and their implications for your data architecture. It will also address how to build and where to buy data fabrics.

Data and analytics leaders must view their data management practice as an evolution, iteratively maturing and expanding based on AI use cases and not a rip-and-replace or one-size-fits-all solution. Join this interactive session to understand the key components in the data management architecture that are foundational to AI-ready data.

Data lakehouses continue to be hyped, but do they replace or complement data lakes and data warehouses? Where do we stand from an architectural perspective? What is hype, and what is real? What should be expected in the coming years?

Data and analytics leaders must view their data management practice as an evolution, iteratively maturing and expanding based on AI use cases and not a rip-and-replace or one-size-fits-all solution. Join this interactive session to understand the key components in the data management architecture that are foundational to AI-ready data.

Data and analytics leaders must view their data management practice as an evolution, iteratively maturing and expanding based on AI use cases and not a rip-and-replace or one-size-fits-all solution. Join this interactive session to understand the key components in the data management architecture that are foundational to AI-ready data.

As data continues to expand, it is becoming more problematic to provide performant query access to entire datasets. The data twin, implemented as a data product, is a representative subset of the entire population that is significantly smaller. It can be reliably generated and consumed for inference and supporting a range of use cases, including exploratory, estimation and hypothesis testing. In this session, attendees will learn what a data twin is and how to implement data twins to support AI initiatives.