How Data Mesh Scales Data Virtualization

Data Mesh is emerging as a transformative approach to accessing data across diverse technologies and platforms, challenging traditional data management paradigms like data virtualization. While data virtualization offers a high-level view of organizational data and is praised for its security, cost-effectiveness, and performance advantages over traditional ETL processes, its scalability is limited when dealing with larger data scopes, often requiring additional technologies to enhance performance. This approach can lead to complex architectures and increased time-to-insight. In contrast, Data Mesh advocates for domain-driven data ownership and leverages a DevOps-like approach, allowing for fast provisioning and distribution of virtualized data copies. It supports seamless access to data regardless of its migration status and presents federated data in a consistent format to front-end applications. Starburst exemplifies this by enabling queries to be split for parallel processing, thus enhancing performance across both cloud and on-premise environments. This aligns with the Data Mesh principle of providing frictionless data access while minimizing the need for data duplication and facilitating scalability in large organizations.