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Answer: Employ a single Delta Lake table that accommodates both streaming writes and batch reads/updates, organizing data by ingestion time.
The optimal strategy involves employing a single Delta Lake table that supports both streaming writes and batch reads/updates, with data partitioned by ingestion time. This approach leverages Delta Lake's ACID transaction capabilities, enabling seamless data access and consistency across real-time and batch processing. By partitioning data based on ingestion time, query performance is optimized for both workload types, eliminating the need for separate storage layers or synchronization mechanisms. This method simplifies data modeling and enhances operational efficiency in a lakehouse environment.
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When designing a lakehouse to accommodate both real-time and batch processing workloads, which strategy ensures data is efficiently accessible across both without redundant storage or processing?
A
Adopt an event sourcing pattern, recording all changes as immutable events in the lakehouse, and creating materialized views for various workload needs.
B
Use separate storage solutions for real-time and batch data, with periodic synchronization between them.
C
Employ a single Delta Lake table that accommodates both streaming writes and batch reads/updates, organizing data by ingestion time.
D
Store streaming data temporarily and batch load it into the lakehouse at predetermined intervals.
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