How is Data Impacted?
When data enters LogScale through ingestion, it undergoes a journey that fundamentally changes its nature - think of it like water flowing through a sophisticated filtration system. This guide explores the transformation process and the implications to data.
Data Transformation
Your raw data starts as a stream of information - which may be unstructured, and inconsistent. During ingestion, LogScale acts as a refinery, processing this raw material into something more valuable.
Standardization of timestamps
Transformation of unstructured log lines
Organization of fields
Positive Transformations
Structured and searchable data
Aligned timestamps
Consistent field normalization
Enhanced context through enrichment
Potential Challenges
Warning
Be aware of these common issues:
Parser misinterpretation
Unexpected field extraction
Time zone misalignment
Data duplication
Real-World Impact
The quality of your ingested data directly affects your ability to find answers during troubleshooting. Well-processed data enables:
Faster searches
More accurate alerts
Better insights
Making It Work For You
Success with data ingestion requires regular monitoring and maintenance:
Monitor ingestion processes
Watch for parsing errors
Validate data regularly
Adjust configurations as needed
Data Immutability
Once ingested, data in Falcon LogScale is immutable. Data can only be deleted under certain conditions and with specific elevated privileges:
By time โ Data is automatically purged at the end of the designated retention period. See Data Retention.
By manual deletion of the repository โ A user with sufficient permissions can delete an entire repository. See Delete a Repository or View.
By API โ A user with specific privileges and administrative power over a repository can leverage the Redact API to remove specific data. See Redact Events API.
All of the above actions can only be performed by authorized users with the specific mentioned permissions tied to specific repositories.