Databricks Certified Data Engineer - Associate

The primary benefit highlighted is that a data lakehouse supports both batch and streaming analytics from the same storage system. Traditional data warehouses are typically designed for batch-oriented workloads and analytical queries on structured data, whereas a lakehouse (for example, Databricks Lakehouse with Delta Lake) unifies data warehousing and data lakes to support streaming ingest/processing and batch analytics together. While snapshot/versioning (time travel) is also a capability offered by some lakehouse implementations (Delta Lake provides time travel), the best single answer for a benefit broadly unavailable in traditional data warehouses is the unified support for both batch and streaming analytics. Other options are incorrect: A is false because traditional warehouses are relational; C is incorrect because lakehouses typically decouple compute and storage rather than coupling them; D is incorrect because lakehouses favor open storage formats (e.g., Parquet, Delta) rather than proprietary formats.