Adaptive thresholding is a term used in computer science and — more specifically — across IT Service Intelligence (ITSI), for analyzing historical data to determine key performance indicators (KPIs) in your IT environment. Among other things, it’s used to govern KPI outliers in an effort to foster more meaningful and trusted performance monitoring alerts.

On the heels of an exciting GA in March and the April announcement of its regional expansion, we are excited to share the latest updates to Splunk Edge Processor that will make it even easier for customers to have more flexibility and control over just the data you want, nothing more nothing less.

Anomaly detection in metrics or time series data is the most used machine learning use case among Splunk Security and Observability customers. Customers are looking for easy-to-use ML-powered high-fidelity anomaly detection, so that they can be alerted at the first sign of a failure point or security incident.

Data has become an essential asset for businesses, driving innovation, improving decision-making, and shaping the future. But how does one effectively manage this valuable resource? This is where Data Lifecycle Management (DLM) comes in — a comprehensive approach to managing data throughout its lifecycle. This blog post will guide you through the ins and outs of DLM, its key stages, benefits, and the tools and technologies that enable successful implementation. Let's dive in!

The control plane and the data plane are two key components of a network, each facilitating the flow of network packets, and each with its own set of functions and responsibilities. Understanding the differences between these two planes helps you design and optimize networks for improved performance, scalability, and security. This article will explore the control and data planes, including their functionalities, fundamental differences and use cases.

Splunk is pleased to announce its latest innovation in data access and processing — Splunk Edge Hub, in partnership with Edge Hub Central.

Imagine your company relies on inaccurate data to drive its strategies, only to realize too late that the information needed to be revised. The consequences could be devastating — missed opportunities, incorrect forecasts, and damaged customer relationships. But by monitoring data, you can understand your company's digital ecosystem comprehensively, make informed decisions, optimize processes, and mitigate risks effectively.

Organizations use the cloud for scalable computing and storage solutions to meet their changing business needs. However, another deployment model is quickly growing in popularity: edge computing. In fact, Gartner estimates that 75% of enterprise-generated data will originate and be processed at the edge by 2025.

As companies embrace containers, microservices, and complex architectural components, systems have grown more and more distributed and unpredictable, increasing the unknown unknowns. How can organizations remain efficient and effective in this type of intricate environment? With observability-driven development.

The cloud is the future — or is it? While statistics show that the public cloud continues to grow, a small but loud group is proudly going in the other direction. David Heinemeier Hansson, the CTO of 37signals — the company behind Basecamp and HEY, among others — recently posted his controversial take on the subject, announcing that the organization would be leaving the cloud.