Data source normalization is a crucial part of achieving maximum success with your Splunk solution. SP6’s Data Source Normalization service is an engagement that normalizes your data sources to ensure data is being aligned to commonly utilized Data Models in accordance with Splunk best practices.
For Splunk technical professionals, this includes evaluating which log feeds align to which Data Models, applying custom parsing, identifying opportunities for data enrichment, and configuring the Data Model acceleration. The Data Source Normalization service remediates issues identified with data sources regarding normalization and CIM compliance. These efforts result in a cohesive, streamlined approach to ingesting and leveraging data in your Splunk environment for optimal performance.
We’ll normalize all applicable log sources to commonly utilized data models.
We’ll support and facilitate the installation and upgrades of supported Splunk TAs.
We’ll develop custom field extractions, field aliases, and/or log parsers as needed.
We’ll perform validation verbosity of raw log sources and latest Data Set schemas.
We’ll configure Data Model Acceleration leveraging engineer expertise.
We’ll incorporate any available data enrichment for applicable log sources and Data Models.
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