Improve Performance - A Prescriptive Splunk Outcome
The Splunk platform is designed to scale but this needs to be done strategically to maintain optimal efficiency and user satisfaction. Likewise, careful planning can guarantee robust data reliability, compliance, and refined knowledge objects. The following three pathways offer numerous strategies to help you implement these features and improve performance:
- Planning for infrastructure and resource scalability
- Improving data management and governance
- Optimizing systems and knowledge objects
By following the guidance in these pathways, you will ensure optimal scalability through precise capacity planning, better architecture design, balanced workloads, and optimized indexing. You will also implement standardized data management procedures for better monitoring and governance. Finally, your will attain top-tier system performance and object efficiency by applying customized configurations, efficient indexing techniques, advanced acceleration strategies, and streamlined data models.
In addition to improving performance, we encourage you to follow the other Splunk Outcome Paths outlined below to build the efficient deployment you need to succeed.
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Need intro to Improve Performance
- Improving data management and governance
- A systematic approach to data management and governance guarantees robust data reliability, compliance, and optimized utilization. Learn how to define your approach in this Splunk Outcome Path.
- Optimizing systems and knowledge objects
- To achieve optimal system performance and knowledge object efficiency, you'll need customized configurations, consistent tuning, streamlined objects, effective summary indexing, and more. Learn how to create these in this Splunk Outcome Path.
- Planning for infrastructure and resource scalability
- You can scale your Splunk environment while maintaining optimal efficiency and user satisfaction. Learn how in this Splunk Outcome Path.