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Answer: Use Azure Databricks to process the data and leverage Delta Lake's versioning capabilities to revert data to a previous state.
Option B is the correct approach as it leverages Delta Lake's versioning capabilities to revert data to a previous state. Delta Lake provides built-in support for data versioning, allowing for easy recovery of data in case of corruption or other issues. This ensures data integrity and reliability in the batch processing solution. Options A, C, and D do not provide the same level of control and flexibility for reverting data to a previous state in the context of a Delta Lake.
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In your batch processing solution, you need to implement a mechanism to revert data to a previous state in case of data corruption or other issues. How would you implement this functionality?
A
Use Azure Data Factory's data flow activities to process the data and store intermediate results in Azure Blob Storage, allowing for data recovery in case of issues.
B
Use Azure Databricks to process the data and leverage Delta Lake's versioning capabilities to revert data to a previous state.
C
Use Azure SQL Database to store the processed data and implement transaction logging to revert data to a previous state.
D
Use Azure Cosmos DB to store the processed data and implement custom backup and restore mechanisms to revert data to a previous state.