Implementing Splunk Data Stream Processor (DSP) 1.2 Course Overview

Target Audience for Implementing Splunk Data Stream Processor (DSP) 1.2

The Implementing Splunk DSP 1.2 course is designed for professionals managing data streams and seeking to leverage Splunk's powerful processing capabilities.

• Data Engineers
• Splunk Administrators
• IT Operations Analysts
• Systems Administrators
• Security Analysts
• DevOps Engineers
• Network Engineers
• Cloud Architects
• Software Developers
• Business Intelligence Professionals
• Data Scientists (interested in streaming machine learning capabilities)
• Technical Managers overseeing data processing infrastructure
• Solutions Architects designing data ingestion and processing systems
• Infrastructure Engineers responsible for maintaining and scaling data platforms

Learning Objectives - What you will Learn in this Implementing Splunk Data Stream Processor (DSP) 1.2?

Introduction to the Course's Learning Outcomes and Concepts

This course offers an in-depth exploration of the Splunk Data Stream Processor (DSP) 1.2, from deployment to advanced pipeline construction, ensuring efficient data processing and integration in Splunk environments.

Learning Objectives and Outcomes:

• Understand Splunk deployment options and the specific challenges they address.
• Articulate the purpose, value, and core concepts of Splunk DSP, including key terminologies.
• Execute the installation and configuration of a DSP cluster, ensuring readiness for data processing tasks.
• Acquire skills to ingest data using the DSP REST API and configure source and sink connections for optimal data flow.
• Develop competency in building basic to advanced DSP pipelines, employing the DSP canvas, SPL2, and relevant commands.
• Apply data transformation techniques using scalar functions and route data effectively to designated sinks.
• Gain expertise in enriching data streams with DSP lookups and Splunk KV Store integrations.
• Integrate third-party data sources like Kafka, and perform data transformation and normalization for various endpoints such as S3.
• Onboard and transform observability data—including logs, metrics, and traces—for use in Splunk indexers and SignalFx.
• Utilize the DSP Streaming ML plugin to apply machine learning algorithms in real-time data streams, enhancing data insights.
• Master the skills necessary to monitor, back up, troubleshoot, scale, and upgrade a DSP environment for sustained performance and reliability.