Implementing Splunk Data Stream Processor (DSP) 1.2

Implementing Splunk Data Stream Processor (DSP) 1.2

Implementing Splunk Data Stream Processor (DSP) 1.2

Upcoming Classes

Online

Instructor-led online training

Location Jun 2021 Jul 2021 Aug 2021 Sep 2021 Oct 2021 Nov 2021 Dec 2021
AMER Pacific Time - Virtual Jun 28 – Jul 1
 Sep 13 – Sep 16
 Oct 25 – Oct 28
AMER Eastern Time - Virtual Jul 13 – Jul 16
 Aug 2 – Aug 5
 Oct 4 – Oct 7
APAC Singapore - Virtual Jul 20 – Jul 23
 Sep 7 – Sep 10
EMEA UK Time - Virtual Jul 26 – Jul 29
 Aug 23 – Aug 26
 Sep 27 – Sep 30
 Oct 25 – Oct 28

Summary

This 4 day course is designed for the experienced Splunk administrators who are new to a Splunk DSP. This hands-on class provides the fundamentals of deploying a Splunk DSP cluster and designing pipelines for core use cases. It covers installation, source and sink configurations, pipeline design and backup, and monitoring a DSP environment.

Description

  • Introduction to Splunk DSP
  • Deploying a DSP cluster
  • Configuring SplunkSources and Sinks
  • Building Pipelines - Basics
  • Building Pipelines - Intermediate
  • Building Pipelines - Advanced
  • Working with 3rd-party Sources and Sinks
  • Working with Metrics and Traces
  • Streaming ML Plugin
  • Monitoring DSP Environment

Duration

4 Days

Objectives

Module 1 – Introduction to DSP

  • Review Splunk deployment options and challenges
  • Describe the purpose and value of Splunk DSP
  • Define DSP concepts and terminologies

Module 2 – Deploying a DSP Cluster

  • List DSP core components and system requirements
  • Describe installation options and steps
  • Check DSP service status
  • Learn to navigate in DSP UI
  • Use scloud

Module 3 – Prepping Sources and Sinks

  • Ingest data with DSP REST API service
  • Configure DSP source connections for Splunk data
  • Configure DSP sink connections for Splunk indexers
  • Create Splunk-to Splunk pass-through pipelines

Module 4 – Building Pipelines - Basic

  • Describe the basic elements of a DSP pipeline
  • Create data pipelines with the DSP canvas and SPL2
  • List DSP pipeline commands
  • Use scalar functions to convert data types and schema
  • Filter and route data to multiple sinks

Module 5 – Building Pipelines - Intermediate

  • Manipulate pipeline options:
    • Extract
    • Transform
    • Obfuscate
    • Reduce payload

Module 6 – Building Pipelines - Advanced

  • Review Splunk lookups
  • Enrich data with DSP lookups
  • Populate KV Store lookups from DSP streams
  • Manipulate pipeline options
    • Aggregate
    • Conditional trigger
  • Introduce the DSP Plugins SDK

Module 7 – Working with 3rd party Sources and Sinks

  • Read from and write data to pub-sub systems like Kafka
  • List sources supported with the collect service
  • Transform data from Kafka and normalize
  • Write to S3

Module 8 – Working with Metrics and Traces

  • Onboard observability data (log, metric, and trace) into DSP
  • Transform metric data for Splunk indexers and Splunk SignalFx
  • Transform trace data for Splunk Infrastructure Monitoring
  • Route metric data to Splunk indexers and SignalFx
  • Send trace data to Splunk SignalFx

Module 9 – Streaming ML Plugin

  • Describe the advantage of using DSP Streaming ML plugin
  • Enable the Streaming ML plugin in DSP
  • List the DSP Streaming ML functions
  • Practice DSP ML algorithms with the ML datagen

Module 10 – Monitoring DSP Environment

  • Back up DSP pipelines
  • Monitor DSP environment
  • Describe steps to isolate DSP service issues
  • Scale DSP
  • Replace DSP master node
  • Upgrade DSP cluster

Prerequisites

Required:

  • Splunk Enterprise System Administration
  • Splunk Enterprise Data Administration

Recommended:

  • Architecting Splunk Enterprise Deployments

Nice to have:

  • Working knowledge of open source projects:
    • Apache Kafka (user level)
    • Apache Flink (user level)
    • Kubernetes (admin level)

Onsite Training

For groups of three or more

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Public Training

AMER Pacific Time - Virtual

AMER Eastern Time - Virtual

APAC Singapore - Virtual

EMEA UK Time - Virtual


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