Preparing to deploy observability use cases in the Splunk platform

1. Prerequisites

The Splunk platform provides comprehensive visibility across modern enterprise environments by combining log analytics, metrics, and tracing data. In order to effectively ingest the initial logging data at scale, a properly implemented Splunk Enterprise or Splunk Cloud Platform environment must be in place. Resources such as the Splunk Success Framework can help to ensure any deployment is aligned with proven best practices.

2.0 Recommended training

• eLearning: Getting data into Splunk - free eLearning
• eLearning: Intro to knowledge objects - free eLearning
• eLearning: Splunk Enterprise installation and configuration - free eLearning
• eLearning: Creating knowledge objects - free eLearning
• eLearning: Creating field extractions - free eLearning
• Splunk Enterprise and Splunk Cloud - Splunk platform education courses

3.0 Resources

• OnDemand Services
  • OnDemand Services Catalog - Splunk platform
• Professional Services
  • Professional Services - Splunk Enterprise Cloud implementation success
  • Professional Services - Splunk optimization check
• Assigned Expert - Services datasheet

4.0 Considerations

Choosing the appropriate core data platform comes down to six primary considerations. All of these are important if you want to be able to work with any data in your organization; regardless of source, format, or time scale, you want to be able to ask any question and get actionable insight.

1. On-premises, cloud or hybrid. Multiple factors determine whether you manage your data on site, through a cloud provider, or a combination of both. Those factors include:
   • Security and compliance requirements
   • Costs of different software licensing models
   • Which skills/functions you want to maintain in your in-house IT team, and which you acquire through your vendors
2. Scalability. A data platform must be able to perform at today’s scale and be adaptable to the inevitable growth of your data stores. The need for scalability is one of the main forces behind the increased adoption of cloud-based data platforms.
3. Flexibility. Flexibility is essential. Can the platform currently serve multiple groups and use cases? Is it relatively straightforward to add new functions and use cases to the platform? Is there a robust ecosystem of applications and add-ons that can support new functions?
4. Usability and breadth. Is the platform you’re considering simple to deploy and configure for users of varying skill levels? What’s the learning curve? Applying data to every decision requires that anyone in your organization, from IT wizards to non-technical employees, are able to work with that data.
5. Security and compliance. Organizations need to ensure that their data is protected to prevent data breaches that dominate headlines and put companies, customers and even nations at risk. That means ensuring that your data platform has robust security features built in, or tools that integrate with your existing security solutions. The same is true for compliance - a data management platform that adheres to the frameworks and guidelines established by a country or region’s regulatory bodies is essential if your organization does business in that country or region.
6. Intelligence and automation. Vast quantities of data - for which a data platform is a requirement - exceed the capabilities of even the most dedicated analysts. Innovations in technology, particularly around machine learning (ML) and artificial intelligence (AI), have created new opportunities for organizations of every size to benefit from data-driven insights.