Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: E - A data lakehouse enables both batch and streaming analytics.

Why E is correct:

• Data lakehouses are designed to support both batch processing (processing large volumes of data at rest) and streaming analytics (processing data in real-time as it arrives)
• Traditional data warehouses are primarily optimized for batch processing and struggle with real-time streaming workloads
• The lakehouse architecture combines the best of data lakes (flexibility, support for various data types) and data warehouses (ACID transactions, performance)

Why other options are incorrect:

• A: Traditional data warehouses already provide relational systems of data management
• B: Version control and snapshots are not unique to lakehouses; some traditional systems also have these capabilities
• C: Coupling storage and compute is actually a characteristic of traditional data warehouses, not a distinguishing benefit of lakehouses
• D: Lakehouses typically use open formats (like Delta Lake, Parquet) rather than proprietary formats, which is actually an advantage over traditional warehouses

Key Lakehouse Benefits:

• Unified platform for BI, ML, and streaming
• Support for diverse data types (structured, semi-structured, unstructured)
• Open data formats and standards
• Cost-effective storage and compute separation
• ACID transactions on data lakes