How does Splunk ensure data integrity during the indexing phase?

How does Splunk ensure data integrity during the indexing phase?

• A. By encrypting data before indexing
• B. By compressing data to save space
• C. By keeping raw data and indexed data separate
• D. By removing duplicates during ingestion

Answer: (C)

Splunk ensures data integrity during the indexing phase primarily by keeping raw data and indexed data separate. This approach allows for the preservation of the original raw data while also creating an optimized version that can be searched efficiently. By maintaining the raw data, Splunk provides users with the ability to access the unaltered information at any point, which is crucial for auditing, compliance, and troubleshooting. This separation also facilitates the normalization and indexing processes without compromising the original data, giving users insight into the completeness and accuracy of the indexed information. Furthermore, it helps in retaining the context of the data, as well as enabling the possibility of re-indexing if necessary or applying different indexing techniques based on user or use-case requirements. While other options, such as data encryption, compression, and duplicate removal, are relevant to data handling and storage efficiency, their roles do not specifically focus on data integrity during the indexing phase in the same way that separating raw and indexed data does.

Splunk ensures data integrity during the indexing phase primarily by keeping raw data and indexed data separate. This approach allows for the preservation of the original raw data while also creating an optimized version that can be searched efficiently. By maintaining the raw data, Splunk provides users with the ability to access the unaltered information at any point, which is crucial for auditing, compliance, and troubleshooting.

This separation also facilitates the normalization and indexing processes without compromising the original data, giving users insight into the completeness and accuracy of the indexed information. Furthermore, it helps in retaining the context of the data, as well as enabling the possibility of re-indexing if necessary or applying different indexing techniques based on user or use-case requirements.

While other options, such as data encryption, compression, and duplicate removal, are relevant to data handling and storage efficiency, their roles do not specifically focus on data integrity during the indexing phase in the same way that separating raw and indexed data does.