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Answer: Create a global indexing and metadata layer that hides physical storage complexities, enabling unified querying.
Establishing a global indexing and metadata layer is pivotal in a federated lakehouse architecture for seamless access and query performance across disparate data domains. This layer abstracts the physical storage details, offering a unified querying interface without the need for knowledge about the data's physical location or format. It centralizes data access management, optimizes query performance by simplifying access to variously stored data, and provides insights into the data landscape for better query execution planning. This approach is ideal for maintaining efficiency and performance in cross-domain analytics.
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In a federated lakehouse architecture designed for cross-domain analytics, what strategy ensures seamless access and optimal query performance across data stored in various locations and formats?
A
Leverage data virtualization to create a logical overlay on physical data stores, enhancing query efficiency based on access patterns.
B
Duplicate data across domains into a central lakehouse, prioritizing accessibility over storage efficiency.
C
Deploy Apache Spark to read diverse data sources and formats, with caching of intermediate results to boost performance.
D
Create a global indexing and metadata layer that hides physical storage complexities, enabling unified querying.