CloudOps Machine Data Analytics: Splunk for CloudOps

CloudOps Machine Data Analytics: Splunk for CloudOps

• discover the key concepts covered in this course
• describe the features of Splunk that help implement observability solutions for cloud operations
• recognize the capabilities of Splunk that help discover problems and use operational intelligence insights to facilitate business productivity
• list the core products and developer tools provided by Splunk that can be used to collect and index data as well as implement visualization services for operational intelligence
• recognize the various flavors of Splunk along with the benefits of using Splunk Cloud
• use Splunk to explore, categorize, analyze data, and generate Sparklines to depict existing patterns
• name the data sources that can be used with Splunk and outline the basic data model that helps define an internal dataset and how data is indexed
• illustrate how the primary interface of Splunk Cloud is used for searching, problem investigation, reporting on results, and administrating Splunk deployments
• describe the essential data ingestion (collection) concepts used by Splunk Cloud to make data available for insight and analysis
• demonstrate how to ingest data in Splunk to organize and mine data and generate operational intelligence
• recognize the role played by Splunk to improve application delivery via the use of CloudOps practices
• compare the concepts of incident and problem management and identify the role played by VictorOps and Splunk to manage incidents and problems in the DevOps lifecycle
• integrate VictorOps with Splunk to manage incidents and build reports with the required or desired insights
• describe the critical challenges of monitoring multi-cloud environments and the approach adopted by Splunk to identify, investigate, and resolve critical issues
• recognize the key infrastructure monitoring capabilities provided by Splunk App for Infrastructure (SAI)
• summarize the key concepts covered in this course

Splunk is a horizontal application with many possible use cases. In this course, you'll explore the core Splunk products, features, and pricing options that can help achieve CloudOps observability, discover problems, collect and index data, gain business-critical insights, and implement a visualization service for cloud operational intelligence.

Additionally, you'll examine the benefits of using Splunk Cloud, the data ingestion concepts used by this product, and its approach to identifying, investigating, and resolving critical issues. You'll also recognize the infrastructure monitoring capabilities provided by Splunk App for Infrastructure.

Moving on, you'll learn to use Splunk to explore, categorize, and analyze data and generate Sparklines. You'll navigate Splunk Cloud's primary interface. You'll ingest, organize, and mine data in Splunk to glean operational intelligence. And lastly, you'll integrate VictorOps with Splunk.

CloudOps Machine Data Analytics: Working with Splunk Components

CloudOps Machine Data Analytics: Working with Splunk Components

• discover the key concepts covered in this course
• describe the data model, datasets, dataset fields, field types, categories, and inheritance used to create reports in Splunk
• use the Data Model Editor in Splunk to design a new data model and add a root event dataset and root search dataset to the data model
• describe the key components of Splunk Search Processing Language that can be used to achieve expected outcomes from datasets
• demonstrate the use of commands in Splunk to transform search results into data structures used to represent statistics and build required data visualizations
• create charts and reports for visualizing ingested data in Splunk
• use Splunk to create dashboards and add reports, charts, and search results to the dashboards
• recognize the best practices that need to be adopted when designing data models in Splunk to ensure the models fulfil reporting requirements
• use Splunk to create pivot reports that reflect aggregation of the values of one column with respect to the values of another column
• define the concept of lookups and describe the different types of lookup configurations that can be created in Splunk to add fields from external data sources
• use Splunk to create and use lookup files to create lookup definitions
• use Splunk to schedule the process of setting up triggers to run reports automatically
• create and configure alerts in Splunk by running search queries and saving the results as alerts
• create and use the search macro in Splunk to implement reusable blocks of search processing language
• summarize the key concepts covered in this course

The components of Splunk provide CloudOps practitioners with reliable methods to give their data meaning and structure in efficient ways. In this course, you'll examine various Splunk components used to create reports, including datasets, data models, and inheritance. You'll also explore the primary components of Splunk's Search Processing Language, some best practices for designing data models with Splunk, and the different types of lookup configurations you can create in Splunk.

You'll then use the Data Model Editor to design a data model and create charts, dashboards, and reports for visualizing ingested data. You'll use commands in Splunk to transform search results into data structures. You'll create pivot reports, lookup files, alerts, and search macros. Lastly, you'll learn how to run Splunk reports automatically.