Fault incidents are inevitable. They occur in any large-scale enterprise IT environment, especially when: In fact, research indicates, more than half (50%) the leaders in tech and business organizations consider the complexity of their data architecture a significant pain point. From an end-user perspective, businesses must overcome complex architecture in order to ensure service delivery and continuity.

Engineering and ITOps teams have an important mission: keeping their software and digital systems performing and reliable. But as we’re about to embrace a new year full of changes, industry shifts, and AI developments, this mission is challenged by increasingly complex environments, technology alternatives, and an overwhelming number of tools available. The result? Overages, tool sprawl, and toil, which all lead to longer times to detect and resolve issues.

The manufacturing industry is at a crossroads. With automation and emerging technologies like AI, organizations are eager to make operational and production processes more efficient. However, for many manufacturers, the rapid pace of digitizing legacy infrastructure and systems has also exposed many unanticipated hurdles, with one of the biggest being the convergence between IT and operational technology (OT).

With M-21-31’s Advanced EL3 requirements now past due, many US Federal Civilian agencies are still looking to close gaps in their Enterprise Logging capabilities. As part of the EL3 requirements, agencies must be finished implementing user behavioral analytics (UBA) that enables: For many organizations that leverage machine learning (ML) to detect anomalous behavior across the network, UBA solutions have become a critical piece of the enterprise security and insider threat puzzle.

Welcome to the era of AI. It’s the technology advancement that motivates and excites me every day as a CTO. Generative AI is already transforming many areas of our lives, from helping us write emails to assisting us with customer service. What waits for us on the immediate horizon? Today, we released our annual predictions series. Splunk’s 2024 Predictions features three editions: Executive, Security and Observability.

There is more data available to us than ever. Storing this data is important — but deciding on the right type of data storage solution is not so clear. This article explores two primary types of big data storage: data lakes and data warehouses. We’ll examine the benefits of each, then discuss the key differences between a data lake and a data warehouse, so you can decide on the best approach for your business.

Imagine this scenario: your platform appears to have an issue. Maybe it has gone down or maybe it has affected a large volume of users or perhaps just a few of those important ones; either way there is a significant problem with it. Users are complaining and are happy to shout about the platform not working on X (formally Twitter).

Have you ever wondered why some once-prominent companies now find themselves less popular, even overshadowed by smaller competitors? A prime example of this shift is Facebook. Although Facebook was the heartthrob of the 2000s, major issues like internet privacy and possible leaking of user records have made users more suspicious. Only 18% of American Facebook users think the platform protects their data and privacy.

In Parsing Domains with URL Toolbox, we detailed how you can pass a fully qualified domain name or URL to URL Toolbox and receive a nicely parsed set of fields that includes the query string, top level domain, subdomains, and more. In this article, we are going to do some nerdy analytic arithmetic on those fields.

The transition to cloud-native architectures has led to an explosion in metrics data, both in volume and cardinality. This necessitates the development of monitoring systems capable of managing large-scale, high-cardinality data to achieve effective observability in these environments . In this blog post, we’ll explore the important role of cardinality in monitoring and observability.