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

## 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

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