The modern data stack is evolving to address the challenges of growing data silos and technical talent shortages, with composable connectivity and self-service development rethinking its architecture. The legacy data stack was built on relational database management systems (RDBMS) and supporting technologies, but it struggled to scale with explosive data growth. Waves of cloud-centric data solutions arrived, but they often created more complexity than simplicity. Today's modern data stack lacks clean connectivity across on-premises and cloud data solutions, leading to business strategy gaps as data users wait for in-house programmers and ad hoc vendor solutions. A new generation of fully managed cloud-first data solutions is emerging, with tools like data pipelines, cloud-based data lakes and warehouses, data transformation tools, and data analysis tools that can unite the modern data stack into a composable architecture. This requires modular design, standards-based connectivity, self-service data access, and location-agnostic deployment to futureproof the modern data stack and manage its growing roster of sources, destinations, and data delivery tasks.