An organization implements a 'data lakehouse' architecture. If a critical analytical query that joins data across both the data lake (object storage) and the data warehouse (relati... An organization implements a 'data lakehouse' architecture. If a critical analytical query that joins data across both the data lake (object storage) and the data warehouse (relational database) suddenly experiences a significant performance degradation, which of the following represents the most likely root cause, assuming no recent changes to data volumes or query complexity?
Understand the Problem
The question describes a data lakehouse architecture experiencing performance degradation in a critical analytical query that joins data across a data lake and a data warehouse. It asks us to identify the most likely root cause, given that data volumes and query complexity haven't changed. The answers provide four possible root causes, one of which must be the most probable.
Answer
Network latency/connectivity issues between the data lake and data warehouse.
The most likely root cause of a sudden performance degradation in a data lakehouse architecture, when querying across the data lake and data warehouse, is network latency or connectivity issues between the two systems. This is especially true assuming no changes in data volume or query complexity.
Answer for screen readers
The most likely root cause of a sudden performance degradation in a data lakehouse architecture, when querying across the data lake and data warehouse, is network latency or connectivity issues between the two systems. This is especially true assuming no changes in data volume or query complexity.
More Information
A data lakehouse combines the functionalities of a data warehouse and a data lake, allowing for both structured and unstructured data analysis. Querying across these different systems requires seamless data transfer, which when disrupted, leads to performance bottlenecks.
Tips
Ensure consistent network monitoring and diagnostic tools are in place to identify and resolve network-related issues promptly. Regularly test connectivity between the data lake and data warehouse to proactively identify potential problems.
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