SPLK-LOGOBS - Using the Splunk Log Observer

Overview

Duration: 5.0 hours

This course is designed for developers responsible for debugging their own applications, and for SREs responsible for troubleshooting performance issues. The Splunk Log Observer is built primarily for DevOps teams working on applications built on modern tech stacks (containerized micro-services). However, the course it can be taken by anyone who wants to view recent log data in a no-code environment. This 1-virtual-day course describes how to use the tool to work with log data using the no-code user interface. You will learn to create, save, and share search filters; and to investigate the shape of your log data. You will analyze logs with aggregation functions and group by rules. And you will create rules to manipulate incoming data, as well as to generate synthetic metrics from log data. All concepts are taught using lectures and scenario-based hands-on activities.

Objectives

Please refer to course overview.

Content

Module 1: Introduction

Describe the "Three Pillars of Observability"
Explain how Splunk navigates between the three data types
Explain at a high level how Splunk collects each data type
Explain what a no-code search is
Describe some use cases for the Log Observer

Module 2: Log Observer Basics

Use the Log Observer to view trends in logs over time
Use an aggregation function to summarize log data
Browse fields and top values for logs
Create a set of filters from field data
Save filter sets
Change the time range for logs displayed
Describe the relationship between the four parts of the Log Observer Interface

Module 3: Advanced Searching

Add multiple search filters using field values and keywords
Create and tag Saved Queries
Create visualizations from aggregate log data
Segment visualization using group by
Use search time rules to temporarily transform incoming data
View and configure Live Tail mode
Restrict time windows for viewing log data in various ways

Module 4: Managing Data Pipelines

Describe the data processing pipeline and data indexing
Explain some use cases for data processing rules
Describe the rule types
Differentiate between index-time and search-time rules
Add a rule to the pipeline or edit an existing rule
Create synthetic metrics from log data
Create rules to determine which data is indexed vs being archived (Infinite Logging)

Module 5: Getting Data In

Explain field types in the Log Observer
Describe the various ways to bring log data into Splunk Observability
Name some of the ways that log data is enriched
Differentiate between log messages and metadata
Describe how metadata is stored and accessed on log messages

Audience

Prerequisites

Prior experience with Splunk Infrastructure Monitoring and/or Splunk APM is recommended

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