Cyber-Physical Systems refer to a system that models, automates and controls the mechanism of a physical system in a digital environment. This is an area of significant growth: the global market for Cyber-Physical Systems (CPS) is expected to grow from around $87 billion in 2022 to over $137 billion by the year 2028 at a CAGR of 7.9%. So, what exactly are cyber-physical systems? Let’s take a look.

Traditional monitoring methods become inefficient as organizations shift from legacy software systems to complex cloud-native architectures. This transition renders these methods less effective, as they no longer provide the critical insights needed. In response, observability engineering has emerged as an important discipline, offering a more comprehensive understanding of modern software systems. This article will take you through the definition, importance, and processes of observability engineering.

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).

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).

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.