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Splunk Platform in Big Data

Splunk Platform in Big Data

$302.00
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Self-paced • Lifetime updates
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Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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What does the Splunk Platform in Big Data course cover?

Splunk Platform in Big Data is covered here in 8 modules: Architecting Scalable Splunk Indexing Infrastructure, Data Ingestion and Forwarder Management at Scale, Search Optimization and SPL Performance Engineering and 5 more. The outline lists 64 specific topics, opening with designing index replication factors and search factor configurations for high availability in multi-site clusters.

How do you approach Splunk Platform in Big Data step by step?

The work is sequenced in 8 stages. It starts with Architecting Scalable Splunk Indexing Infrastructure, moves through Data Ingestion and Forwarder Management at Scale and Search Optimization and SPL Performance Engineering, and ends at High Availability, Disaster Recovery, and Upgrade Management. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Splunk Platform in Big Data course?

Module 1 is Architecting Scalable Splunk Indexing Infrastructure. It works through designing index replication factors and search factor configurations for high availability in multi-site clusters., partitioning data by source type, index, and geography to balance ingestion load and optimize retention policies., selecting appropriate hardware specifications for indexers based on daily ingest volume and peak search concurrency. and 5 more.

How is the Splunk Platform in Big Data course delivered?

The Splunk Platform in Big Data course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Splunk Platform in Big Data course cost?

The Splunk Platform in Big Data course is $300 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Security Big Data Engineering with Splunk, Platform Solution in Big Data Dataset, IoT Platform in Big Data Dataset, Big Data Platform As A Service Toolkit.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the design, deployment, and operational governance of Splunk in complex enterprise environments, comparable to a multi-phase infrastructure modernization program involving data platform architecture, security engineering, and compliance integration across distributed systems.

Module 1: Architecting Scalable Splunk Indexing Infrastructure

  • Designing index replication factors and search factor configurations for high availability in multi-site clusters.
  • Partitioning data by source type, index, and geography to balance ingestion load and optimize retention policies.
  • Selecting appropriate hardware specifications for indexers based on daily ingest volume and peak search concurrency.
  • Implementing indexer clustering with rolling restart strategies to minimize downtime during upgrades.
  • Configuring cold-to-frozen storage transitions using S3 or HDFS with lifecycle policies aligned to compliance requirements.
  • Optimizing bucket size and rollover settings to balance search performance and storage overhead.
  • Integrating forwarder-to-indexer TLS configurations to enforce encrypted data transmission at scale.
  • Validating indexer write performance under burst ingestion scenarios using synthetic load testing.

Module 2: Data Ingestion and Forwarder Management at Scale

  • Deploying universal forwarders using configuration management tools (e.g., Ansible, Puppet) across heterogeneous server fleets.
  • Configuring modular inputs for custom application logs with scripted input throttling to prevent resource exhaustion.
  • Managing forwarder-to-indexer load balancing with automatic failover across multiple indexers.
  • Implementing data filtering at the forwarder level using props.conf and transforms.conf to reduce ingest costs.
  • Monitoring forwarder health and data latency via internal logs and custom alerts for early anomaly detection.
  • Securing forwarder communications using certificate pinning and role-based access to deployment servers.
  • Handling time zone and timestamp extraction mismatches for multi-region applications using advanced timestamp parsing rules.
  • Scaling deployment server capacity to support thousands of forwarders with staged app push policies.

Module 4: Search Optimization and SPL Performance Engineering

  • Refactoring SPL queries to minimize event scanning using targeted index and source filtering early in the pipeline.
  • Utilizing summary indexing for pre-aggregating high-volume data used in recurring executive reports.
  • Diagnosing slow searches using Job Inspector metrics to identify bottlenecks in command execution and data distribution.
  • Implementing search head clustering with distributed load balancing to handle concurrent user demand.
  • Setting search time and result limits at the role level to prevent resource monopolization.
  • Designing accelerated data models with appropriate granularity to balance dashboard responsiveness and system load.
  • Using tstats and mcstats for efficient statistical queries on indexed fields instead of raw event processing.
  • Managing concurrent search throttling and memory quotas on search heads to maintain system stability.

Module 5: Enterprise Security Monitoring with Splunk ES

  • Integrating Splunk Enterprise Security with threat intelligence platforms using STIX/TAXII feeds for dynamic lookups.
  • Configuring correlation searches with risk-based scoring and adaptive response actions for incident triage.
  • Normalizing logs from firewalls, EDR, and IAM systems using Common Information Model (CIM) mappings.
  • Designing custom notable event workflows with escalation paths and ticketing integration via ServiceNow or Jira.
  • Validating detection logic using unit testing frameworks like pytest-splunk-addon for consistent rule deployment.
  • Managing asset and identity list updates from Active Directory and CMDB sources for accurate context enrichment.
  • Implementing role-based access controls for security content to separate analyst, responder, and admin privileges.
  • Conducting purple team exercises to test detection coverage and tune false positive rates in correlation searches.

Module 6: Compliance, Auditing, and Data Governance

  • Enabling audit logging for all user activities and system configurations to support forensic investigations.
  • Implementing data retention policies aligned with GDPR, HIPAA, or PCI-DSS using index-based lifecycle rules.
  • Generating compliance reports for access reviews, privileged activity, and data exports using scheduled saved searches.
  • Configuring field-level encryption for sensitive data such as PII or credentials in logs.
  • Validating data integrity using checksums and monitoring for log tampering via internal audit trails.
  • Restricting search capabilities for regulated data using search filters and role-based data models.
  • Integrating with external key management systems (KMS) for encryption key rotation and auditability.
  • Documenting data lineage from ingestion to reporting for regulatory audits and third-party assessments.

Module 7: Splunk Integration with Big Data Ecosystems

  • Exporting Splunk data to Hadoop or Delta Lake using Splunk DB Connect with incremental pull strategies.
  • Ingesting data from Kafka topics using Splunk Connect for Kafka with schema registry integration.
  • Querying external data in S3 via Splunk SmartStore with appropriate cache sizing and manifest tuning.
  • Using Spark-Splunk connectors to enrich big data pipelines with Splunk-derived insights.
  • Implementing bi-directional alerting between Splunk and Apache NiFi for data flow monitoring.
  • Staging high-volume IoT telemetry in Kinesis before batch ingestion into Splunk via Lambda functions.
  • Optimizing SmartStore performance by tuning remote storage latency and hot bucket retention.
  • Validating data consistency across Splunk and data lake zones using reconciliation jobs.

Module 8: Performance Monitoring and Capacity Planning

  • Tracking daily ingest trends and forecasting indexer capacity needs using historical growth models.
  • Monitoring indexer CPU, disk I/O, and memory usage to identify hardware bottlenecks.
  • Setting up alerts for license quota consumption with tiered notifications at 70%, 85%, and 95% thresholds.
  • Conducting search head CPU profiling during peak usage to identify inefficient dashboards.
  • Measuring indexing queue depth to detect backpressure during high-volume events.
  • Right-sizing search head and indexer fleets using Splunk’s Capacity Planning Tool with real ingest profiles.
  • Correlating system performance metrics with user satisfaction indicators like search timeout rates.
  • Planning for seasonal spikes (e.g., fiscal year-end, cyber events) with buffer capacity and autoscaling policies.

Module 9: High Availability, Disaster Recovery, and Upgrade Management

  • Designing multi-datacenter indexer cluster replication with site affinity and failover routing.
  • Implementing cold backup strategies for critical configurations using version-controlled deployment apps.
  • Executing rolling upgrades for Splunk components with pre- and post-validation checklists.
  • Testing disaster recovery runbooks with simulated indexer site outages and data rebalancing.
  • Validating search head cluster failover behavior under node termination conditions.
  • Managing app compatibility across Splunk versions using staging environments and automated testing.
  • Coordinating maintenance windows with business units to minimize impact on critical monitoring.
  • Archiving and decommissioning legacy indexes with data migration and stakeholder sign-off processes.
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