The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time-consuming. A logical data warehouse is a modern data architecture that allows organizations to leverage all of their data irrespective of where the data is stored, what format it is stored in, and what technologies or protocols are used to store and access the data.
Watch this session to understand:
- What is a logical data warehouse and how to architect one
- The benefits of logical data warehouse – speed with agility
- Customer use case depicting logical architecture implementation