Analytics

Logging in a DevOps environment: what you should know

DevOps is the new normal, and cloud here is to stay – sound familiar? When you combine the two and distill the technology at the core, what you end up with is the realization of the importance of logs and log management. This is because logs at multiple levels help DevOps teams understand their application and even allow them to detect and address application issues before being promoted into production.

Zero Trust Security: Supporting a CARTA approach with Anomaly Detection

Learn how Anomaly Detection supports, what Gartner has termed, a continuous adaptive risk and trust assessment (CARTA) when building a CaaS platform using Kubernetes. Anomaly Detection expands the zero trust network security model and continuously assess the application and network risk that enables adaptive policy adjustments.

3 Reasons Log Management is Critical for Business Intelligence

Log management is the answer to all of your digital transformation woes. No, hear me out. At its heart, log management is the (challenging) task of collecting and storing all machine-generated data from across your entire enterprise into a common repository. If this collection doesn’t happen, or if log collection is limited to certain datasets, there’s little chance of deriving those high value insights you dream of.

Machine data processing and 5G, IoT, and AI at Mobile World Congress 2019

One thing that’s become evident to me after years attending Mobile World Congress is that, in fact, there are several events running in parallel, with a few common denominators: network technology providers, device manufacturers, telecom operators, and services companies all come to Barcelona to present and demonstrate the latest and greatest of the year’s dominating trends.

Operational Data: A Roadmap to Value Creation

Data growth is running at close to seven exabytes per day; estimates are that in three to five years' time, growth will be closer to 15 to 25 exabytes per day. Yet many organizations fail to realize the business value of their data, lack the tools and processes to collect and analyze data more effectively, and do not understand how to calculate its return on investment (ROI) potential.