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Answer: Simulating geo-distributed data sources with Azure Event Hubs in different regions, directing data to a single Databricks workspace, and monitoring latency and throughput.
The most suitable approach for designing a load test to validate performance across different regions in a global Azure Databricks solution is to simulate geo-distributed data sources with Azure Event Hubs in different regions, direct data to a single Databricks workspace, and monitor latency and throughput. This method allows for the simulation of real-world scenarios where data is ingested from various locations, tests the solution's efficiency in processing data from multiple regions, and helps identify potential performance issues by monitoring key metrics such as latency and throughput.
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How would you design a load test to validate the performance of a global Azure Databricks solution handling data ingestion and processing from geographically distributed sources?
A
Manually distributing datasets across regions and observing the impact on a centralized Databricks workspace without automation.
B
Utilizing a VPN to mimic geographical latencies and network conditions while ingesting data into a central Databricks workspace for processing.
C
Deploying identical Databricks workspaces in multiple Azure regions, using Azure Traffic Manager to simulate geographically distributed data ingestion and analyzing regional performance metrics.
D
Simulating geo-distributed data sources with Azure Event Hubs in different regions, directing data to a single Databricks workspace, and monitoring latency and throughput.