After deploying a new data pipeline in Azure Databricks intended to manage a substantial rise in data volume, what approach would you take to verify its scalability and performance under anticipated future data loads?

Real Exam

Gradually increase the data volume processed by the pipeline in stages, employing Azure Monitor to observe performance metrics.

3.3%

Use historical data alongside predictive analytics in Databricks to forecast future data volumes and evaluate the pipeline's scalability with simulated data.

15.0%

Perform a manual test run of the pipeline with a sample dataset adjusted to the expected future volume, noting resource usage and throughput.

10.0%

Apply Azure Load Testing to mimic higher data volumes, overseeing the pipeline's performance and dynamically adjusting resources as required.

71.7%

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After deploying a new data pipeline in Azure Databricks intended to manage a substantial rise in data volume, what approach would you take to verify its scalability and performance under anticipated future data loads?