In the context of Azure Databricks and Spark, schema evolution is a critical feature for managing streaming data. Considering a scenario where a financial institution streams transaction data that may evolve over time due to regulatory changes, which of the following best describes the importance of schema evolution and its correct implementation to ensure seamless data processing? Choose the best option from the four provided.

Simulated

Schema evolution is merely a feature that allows the schema of a dataset to be viewed over time without any impact on data processing.

12.3%

Schema evolution is essential for streaming data as it dynamically adapts to schema changes, ensuring continuous data processing without manual intervention.

53.4%

Schema evolution can be manually implemented by stopping the streaming job, updating the schema, and then restarting the job.

6.9%

Schema evolution is not necessary for streaming data as the schema is fixed at the start of the streaming job and does not change.

27.5%

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