Implementing Azure Data Lake Gen 1 Storage for Big Data
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A company uses Azure Data Lake Gen 1 Storage to store big data related to consumer behavior.
You need to implement logging.
Solution: Configure an Azure Automation runbook to copy events.
Does the solution meet the goal?
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A. B.B
Instead configure Azure Data Lake Storage diagnostics to store logs and metrics in a storage account.
Note:
You can enable diagnostic logging for your Azure Data Lake Storage Gen1 accounts, blobs, files, queues and tables.
Diagnostic logs aren't available for Data Lake Storage Gen2 accounts [as of August 2019].
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-diagnostic-logs https://github.com/MicrosoftDocs/azure-docs/issues/34286The proposed solution to implement logging for storing big data related to consumer behavior in Azure Data Lake Gen1 storage is to configure an Azure Automation runbook to copy events. However, this solution is insufficient because it only copies events, but it does not log any information about the data being stored in the Azure Data Lake Gen1 storage.
Logging is the process of recording information about events and activities that occur in an application or system. The purpose of logging is to help troubleshoot problems and provide visibility into system behavior.
To implement logging for Azure Data Lake Gen1 storage, we need to use Azure Data Lake Analytics (ADLA) or Azure HDInsight. ADLA provides a logging feature that can be used to log activities and events for an Azure Data Lake Gen1 storage account. By enabling logging for the storage account, we can capture the details of all operations performed on the storage account, including file creations, modifications, and deletions.
In addition to ADLA, Azure Monitor can be used to monitor the Azure Data Lake Gen1 storage account. Azure Monitor provides insights into the health and performance of the storage account by collecting data from various sources and presenting it in
