Explanation

The correct answer is D because a data lakehouse is specifically designed to address the needs of both data engineering (ETL/ELT workloads) and data science/analytics workloads by:

• Storing unstructured data: Like traditional data lakes, it can handle various data formats including unstructured data
• ACID compliance: Like traditional data warehouses, it provides transactional consistency and reliability

This combination allows data architects to:

• Support data engineering workloads that require flexible data ingestion and processing
• Support analytics workloads that require reliable, consistent data for reporting and analysis
• Eliminate the need to maintain separate data lake and data warehouse systems

Why other options are incorrect:

• A: Data lakehouses still require proper data modeling for optimal performance
• B: While governance is important, combining compute and storage isn't the primary feature that addresses both workload types
• C: Autoscaling is a cloud feature, not specific to lakehouse architecture
• E: Data lakehouses can exist on-premises or in the cloud, not exclusively in the cloud