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Answer: A data lakehouse enables both batch and streaming analytics.
A data lakehouse enables both batch and streaming analytics, distinguishing it from traditional data warehouses. Key features include: - **Transaction support**: Ensures consistency with ACID transactions for concurrent reads/writes. - **Schema enforcement and governance**: Supports schema evolution and integrity, with robust governance. - **BI support**: Allows BI tools to use source data directly, reducing latency and costs. - **Decoupled storage and compute**: Scales efficiently for more users and larger data sizes. - **Openness**: Uses open storage formats like Parquet, accessible by various tools. - **Diverse data types and workloads**: Supports everything from unstructured data to machine learning. - **End-to-end streaming**: Facilitates real-time data applications without separate systems.
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How does a data lakehouse differ from a traditional data warehouse?
A
A data lakehouse utilizes proprietary storage formats for data.
B
A data lakehouse enables both batch and streaming analytics.
C
A data lakehouse provides a relational system of data management.
D
A data lakehouse captures snapshots of data for version control purposes.
E
A data lakehouse couples storage and compute for complete control.