Operations | Monitoring | ITSM | DevOps | Cloud

Anomaly detection is one of the most common problems that Splunk users are interested in solving via machine learning. This is highly intuitive, as one of the main reasons our Splunk customers are ingesting, indexing, and searching their systems’ logs and metrics is to find problems in their systems, either before, during, or after the problem takes place. In particular, one of the types of anomaly detection that our customers are interested in is time series anomaly detection.

The number of mobile game users is expected to increase to 2.3 billion users by 2027, with a CAGR of 7.08%. The resulting projected market volume is a staggering $376.7 billion by 2027. Competition is fierce, and differentiation is key to winning out in this rapidly growing market. To understand their users and build better games, gaming companies need to use data analytics to interpret how players interact with their games. Effective use of video game data can help companies.

Kubernetes is the leading container orchestration platform and has developed into the backbone technology for many organizations’ modern applications and infrastructure. As an open source project, “K8s” is also one of the largest success stories to ever emanate from the Cloud Native Computing Foundation (CNCF). In short, Kubernetes has revolutionized the way organizations deploy, manage, and scale applications.

Collecting and processing logs, metrics, and application data from endpoints have caused many ITOps and SecOps engineers to go gray sooner than they would have liked. Delivering observability data to its proper destination from Linux and Windows machines, apps, or microservices is way more difficult than it needs to be. We created Cribl Edge to save the rest of that beautiful head of hair of yours.

Splunk is the platform for a million use cases, used to investigate operational data across security, observability, fraud, business intelligence and many other domains. But, in my time at Splunk, I’ve come to realize that all of our customers face challenges that stem from the same core problem: Within exploding data volumes, finding the anomalously behaving entities that are most threatening to the resilience of their organization.

Artificial intelligence (AI) has emerged as a transformative force, empowering businesses and software engineers to scale and push the boundaries of what was once thought impossible. However as AI is accepted in more professional spaces, the complexity of managing AI systems seems to grow. Monitoring AI usage has become a critical practice for organizations to ensure optimal performance, resource efficiency, and provide a seamless user experience.

In the face of an ever-evolving cybersecurity landscape, Splunk never rests. Today, we're ecstatic to share the release of Splunk User Behavior Analytics (UBA) 5.3, delivering power and precision in one package, and pushing the boundaries of what's possible in user and entity behavior analytics.

In the world of observability, having the right amount of data is key. For years Apica has led the way, utilizing synthetic monitoring to evaluate the performance of critical transactions and customer flows, ensuring businesses have important insight and lead time regarding potential issues.

Application performance monitoring (APM) is much more than capturing and tracking errors and stack traces. Today’s cloud-based businesses deploy applications across various regions and even cloud providers. So, harnessing the power of metadata provided by the Elastic APM agents becomes more critical. Leveraging the metadata, including crucial information like cloud region, provider, and machine type, allows us to track costs across the application stack.