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A data engineering team is transitioning their analytics workloads from an on-premises SQL database to the Databricks Lakehouse Platform to enhance their reporting and dashboarding capabilities with Databricks SQL. Considering the need for cost efficiency, compliance with data governance policies, and the ability to scale for large datasets, which of the following statements accurately describes the operational mechanism and advantages of Databricks SQL in this scenario? (Choose one correct option)
A
Databricks SQL introduces a new query language that diverges from standard SQL, necessitating additional training for the team, but compensates with unparalleled performance and scalability by utilizing a unique database engine not found in traditional SQL databases.
B
Databricks SQL supports standard SQL queries on data within the Lakehouse, offering a user-friendly interface that maintains compatibility with existing skills while significantly improving performance and scalability for analytics workloads through optimized distributed processing of big data.
C
Databricks SQL is restricted to querying data exclusively in managed Delta tables, with its efficiency constrained by the storage format, thereby not being as effective for real-time analytics as traditional SQL databases.
D
Databricks SQL is tailored for minor, sporadic queries and lacks the capability to integrate with BI tools or support advanced analytics, rendering it inferior to traditional SQL databases for comprehensive data analysis tasks.
Explanation:
Databricks SQL facilitates the execution of standard SQL queries on the Databricks Lakehouse Platform, combining the familiarity of SQL with the enhanced performance and scalability needed for big data analytics. Its distributed processing capabilities are optimized for handling large-scale data efficiently, making it a superior choice for modern data engineering tasks, including seamless integration with BI tools and support for complex datasets, all while adhering to cost and compliance requirements.