Course Content
This 4.5-hour module describes how to use the tool to work with log data using the no-code user interface. Learn to create, save, and share search filters, and to investigate the shape of your log data. Analyze logs with aggregation functions and group by rules. Create rules to manipulate incoming data, and generate synthetic metrics from log data.
All concepts are taught using lectures and scenario-based hands-on activities.
Who should attend
This module 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 microservices). However, the module can be taken by anyone who wants to view recent log data in a no-code environment.
Prerequisites
Prior experience with Splunk Infrastructure Monitoring and/or Splunk APM is recommended
Course Objectives
- View log data
- Describe how log data is parsed and structured in the tool
- Create filters for log data; save and reuse these filters
- Investigate the shape of log data with the Log Observer
- Analyze data with aggregation functions and group by rules
- Manage the data pipeline using rules
- Describe ways to get data in
Outline: Using the Splunk Log Observer (USLO)
Topic 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
Topic 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
- Change the time range for logs displayed
- Describe the relationship between the four parts of the Log Observer Interface
Topic 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
Topic 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)
Topic 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