Prerequisite: None

David Loshin

President

Knowledge Integrity

Modernizing to the cloud affords opportunities for decoupling data storage from computation, which enables increased flexibility in managing, sharing, and analyzing corporate data using both cloud-native data warehouses and cloud-based data lakes. However, in a modern analytics environment, neither the data warehouse nor the data lake alone can satisfy the expanding needs of a growing diverse community of data consumers.

In this talk, David Loshin will explore a blended approach referred to as a data lakehouse that enables a data warehouse and a data lake to work in a complementary manner.  A data lakehouse uses a harmonized semantic layer over structured, semistructured, and unstructured data to establish a foundation for cloud-based business intelligence and analytics. By blending the best of both a data warehouse and a data lake, the data lakehouse concept simplifies data management and governance across an increasingly hybrid landscape while enabling greater downstream data consumer capability. A data lakehouse minimizes data copying, reduces data latency, and improves data reuse.

David Loshin will discuss:

• Benefits of separating data from computing resources
• Structured schemas and virtualization
• Integrated governance over management and access to data in object storage
• Support for query access to both structured and semistructured data
• Federated querying across a distributed environment