Data integration tools help organizations access, process, move and transform data. They support use cases like data engineering, modern data architecture, less-technical/business user support and operational data integration. In this session, we'll present the latest Magic Quadrant for Data Integration Tools, discussing vendors and technologies to help you choose the best tool for your needs.

Data integration is a core component of D&A, and it is continuously transforming. This session provides guidance on how best practices for data engineering are evolving to improve data integration and support AI initiatives. It also examines the trends guiding data integration technology, including how data integration tools are leveraging AI features.

Data engineering faces increasing pressure to deliver AI-ready data that meets agentic AI requirements. This session explores changes in data engineering functions, practices, architectures and technologies.

D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.