Most analytics architectures were built for a world where yesterday’s data was good enough. That assumption no longer holds.
Today, enterprises face the same challenge in different forms. Legacy data warehouses are costly, vendor-locked, and not built for AI. Cloud data warehouses offer greater flexibility but often introduce unpredictable consumption-based costs, limited deployment options, and complex extract, transform, and load (ETL) pipelines that consume valuable engineering resources. The result is that data scientists and AI applications are often forced to work with stale, outdated data.
This video explores how converged analytics removes these constraints by unifying transactional and analytical workloads on a single platform. Built on EDB Postgres AI, converged analytics creates a unified analytical data estate that delivers real-time insights, reduces data movement, simplifies architecture, and provides a scalable foundation for AI-driven applications.
