DevOps Blogs

In the ever-evolving digital ecosystem, where the pace of innovation is relentless, organizations face the dual challenge of managing escalating data volumes while simultaneously enhancing resilience and cost efficiency. Embracing modern data approaches presents a compelling solution, offering the promise of rebalancing the use of the Splunk Platform to enhance digital resilience. Let's delve into why modernizing data strategies is not just an option but a necessity in today's tech landscape and later in this blog we’ll explore the 3 strategies you can adopt to improve your Observability while rebalancing your use of the Splunk Platform.

Splunker Gabriela Parker explains how Splunk Observability Cloud makes the process of testing and review of new code easy for developers.

Splunk Observability Cloud has built-in capabilities to help improve developer experience and productivity.

Splunker Jennifer Elkhouri explains how Splunk On-Call relieves on-call stress: clear alerting practices and defined workflows mitigate developer team burdens.

Splunkers Joanna Zouhour and Navtej Singh introduce Splunk's Archived Metrics, storing data affordably, enhancing accessibility, and reducing costs in Metrics Pipeline Management.

OpenTelemetry context with baggage can help you quickly find value, errors and maybe your luggage.

Splunk’s observability strategy has always put AI functionality at the centre. We have always recognised that, in order to make actionable sense of full fidelity data metric, event, log, and trace data streams, human cognition requires an automated assist which is precisely what AI brings to the table. As a result, throughout our observability portfolio, customers will find a variety of machine learning and pattern discovery algorithms being put to work, separating signals from noise, surfacing patterns of correlation, diagnosing root causes, and enabling remedial responses to incidents. AI, itself, is, of course, evolving at a rapid clip and with AI Assist, Splunk adds Generative or linguistic AI functionality to the mix. But what is linguistic AI and how does it relate to the non-linguistic or Foundational AI that Splunk has deployed in its products to date?

Application Performance Monitoring (APM) as a discipline and as a collection of supporting technologies has evolved rapidly since a distinct recognisable market for APM products first emerged in the 2007 - 2008 time frame. While there are many who would argue that APM has mutated into or been replaced by Observability, it makes more sense to see APM as one of many possible use cases now able to exploit the functionalities that Observability brings to the table - particularly when combined with AI.

If you're building a new application or enhancing an existing one, consider adopting the OpenTelemetry Logs Data Model's Log and Event Record Definition.