Summary
Amidst the ever-increasing data volume, variety, and velocity, new technologies and methodologies continue to emerge to navigate the complexities of modern data landscapes.
When use cases call for a scalable architecture capable of handling many source systems, we can look to Data Vault—a robust framework for constructing scalable and auditable data warehouses, emphasizing efficient, insert-only data loading. For data marts, we compared the star and snowflake schemas, highlighting their distinct advantages in shaping them for optimized self-service and analytics.
Furthermore, we addressed the organizational challenges of managing data in distributed enterprise teams by introducing Data Mesh, a paradigm-shifting framework that advocates for decentralized data ownership, treating data as a product, and fostering a culture of collaboration and self-service.
For big data use cases that go beyond traditional warehousing capabilities due to variable data formats...
