An organization is transitioning from a traditional data warehouse and a data lake to a more unified analytics platform using Databricks. They are facing challenges with data quality, governance, and the ability to perform advanced analytics across all data types. The organization requires a solution that not only addresses these challenges but also supports ACID transactions, schema enforcement, and fine-grained governance to ensure high-quality analytics. Considering the need for scalability, flexibility, and cost-effectiveness, which of the following options best describes the architecture that meets these requirements? (Choose one correct option)
A
A data lakehouse stores all data in its raw format like a data lake, but unlike a data warehouse, it does not support ACID transactions or schema enforcement, making it less suitable for high-quality analytics and governance.
B
A data lakehouse combines the scalability and flexibility of a data lake with the data management features of a data warehouse, such as ACID transactions, schema enforcement, and fine-grained governance, thereby improving data quality and enabling unified analytics across structured and unstructured data.
C
A data lakehouse is essentially a data warehouse that can store unstructured data, but it does not provide the scalability or cost benefits of a data lake, nor does it improve data governance compared to a data lake.
D
A data lakehouse is a data lake with added support for SQL queries, but it does not address data quality or governance issues, as it lacks features like data cataloging and access controls.
Explanation:
A data lakehouse architecture, as implemented in the Databricks Lakehouse Platform, merges the low-cost storage and flexibility of data lakes with the robust data management and governance features of data warehouses. This includes support for ACID transactions, schema enforcement, data cataloging, and fine-grained access controls. These capabilities address the data quality and governance limitations of traditional data lakes, while enabling unified analytics across all data types, which is not possible with a traditional data warehouse alone.
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