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Sometimes you have data. It’s great data, it’s consistent data, and it would just be a heck of a lot more useful if Splunk knew each and every field.
You could always do it old school and use Splunk’s built in Interactive Field Extractor (also known as IFX). Upside: it’s easy. Downside: you’ll need to extract each field individually. And if your data has, like, twenty columns, that’s a lot of extracting you’re doing. there’s a faster way.
If your data is delimited, there’s an easier way to teach Splunk to understand it. As long as your data is consistently delimited…say with a space, comma, or tab…you can teach Splunk how to separate the data and how to label each field.
For example, consider the following data:
Sondra Russell,srussell@splunk.com,Sales Engineer Blondra Blussell,brussell@splunk.com,Senior Sales Engineer
This data is comma delimited and the fields are: name, email, role. So, here’s what you do:
| extract reload=T
[addressbook_fields] DELIMS="," FIELDS = "name","email","role"
[addressbook] SHOULD_LINEMERGE = False pulldown_type = 1 REPORT-getfields = addressbook_fields
For more documentation on this process:
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Thanks!
Sondra Russell
The Splunk platform removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.
Founded in 2003, Splunk is a global company — with over 7,500 employees, Splunkers have received over 1,020 patents to date and availability in 21 regions around the world — and offers an open, extensible data platform that supports shared data across any environment so that all teams in an organization can get end-to-end visibility, with context, for every interaction and business process. Build a strong data foundation with Splunk.