At.conf23, we announced the preview release of Splunk AI Assistant - Splunk's first offering powered by generative AI. This app offers an intuitive and easy-to-use chat experience to help you translate a natural language prompt into SPL query that you can execute or build on, all within a familiar Splunk interface. Splunk AI Assistant also explains what a given SPL query is doing in plain English with a summary as well as a detailed breakdown of the query.

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.

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.

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!

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.

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.

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.

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.

Imagine you have a network, whether it's a LAN or a vast enterprise-level network spread across different locations. Now, picture yourself wanting to monitor and analyze the data flow within that network. That's where network telemetry comes into play. Network telemetry is a group of techniques that allow you to understand better what's happening within networks. It's like watching the network's pulse to keep track of its health and performance. Read on to learn more about the network telemetry landscape.

In today's post-pandemic digital landscape, web analytics is projected to have a market size of US$13.4 Billion by 2030. This means that understanding website data and user behavior is crucial for any business wanting to succeed. Businesses can collect, analyze, and report website data through well-executed web analytics strategies to optimize the user experience and meet business objectives.

Information Technology Operations Analytics (ITOA) is an analytics technology that uses datasets generated by IT systems to improve their efficiency and effectiveness as part of the practice known as IT operations management (ITOM). The primary goal of ITOA is to make IT operations more effective, efficient, faster and more proactive through the use of an organization’s own machine data.

CloudOps (or Cloud Operations) is a combination of network, security, performance, device management, help desk and other tasks that keep cloud-native applications and underlying infrastructure up and running.

Event-Driven Architecture (EDA) is a modern approach to designing distributed systems with loosely coupled components. EDA has gained popularity in many industrial applications due to its flexibility, performance and scalability. This article offers a comprehensive overview of Event-Driven Architecture (EDA), explaining its key components and the patterns used. I’ll also cover the use cases of EDA and the benefits and challenges of implementing it.

Did you see the global COVID pandemic coming when you heard about the first cases? Probably not, even if you tried. As the physicist Albert A. Bartlett pointed out back in 1976, human beings tend to think in linear terms. The effects of large changes in scale are frequently beyond our powers of perception and even our imagination. It is the same challenge highlighted today by the cumulative effects of climate change and the subsequent tipping points.