The fillnull command replaces null or missing values in specified fields with a given replacement value. In this example, the command sets the src field to "Unknown" wherever it is null. It does not affect other fields or convert values from "Unknown" back to null.
Reference:
Splunk Power User Study Guide, Search Commands Section
Splunk Docs: fillnull Command
"fillnull replaces null field values with a specified string or number for a given field or fields."

When using the Field Extractor (FX) in Splunk to perform field extraction, any consistent character can be used as a delimiter. The Field Extractor allows users to define how fields areseparated in the raw event data, and as long as the delimiter is consistent, the FX tool can parse and extract the fields correctly.
References:
Splunk Docs: Field Extractor
Splunk Answers: Field extraction delimiters

A delimiter field extraction is a method of extracting fields from data that uses a character or a string to separate fields in each event. A delimiter field extraction can be performed by using the Field Extractor (FX) tool or by editing the props.conf file. A delimiter field extraction can be applied to any file format that uses a delimiter to separate fields, such as CSV, TSV, PSV, etc. A CSV file is a comma-separated values file that uses commas as delimiters. Therefore, a CSV file can be extracted using a delimiter field extraction.

Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Pivot/IntroductiontoPivot Pivot is used for creating reports and dashboards. Pivot is a tool that allows you to create reports and dashboards from your data models without writing any SPL commands. Pivot can help you visualize and analyze your data using various options, such as filters, rows, columns, cells, charts, tables, maps, etc. Pivot can also help you accelerate your reports and dashboards by using summary data from your accelerated data models.
Pivot is not used for creating datasets or data models. Datasets are collections of events that represent your data in a structured and hierarchical way. Data models are predefined datasets for various domains, such as network traffic, web activity, authentication, etc. Datasets and data models can be created by using commands such as datamodel or pivot.