The constant evolution of security threats has long-since made preventing cyber-attacks and network intrusion attempts a nearly impossible task. Real threats are often hard to identify among a multitude of false alarms, and many experts understand that a well-integrated and fully-automated threat intelligence strategy is the best approach. Nevertheless, 70% of security industry professionals still believe threat intelligence to be too complex and bulky to provide actionable insights.

You know what they say: you can’t fix what you can’t find. That’s what makes log management such a critical element in the DevOps process. Logging provides key information for software developers on the lookout for code errors. While working on their third startup in 2013, Chris Nguyen and Lee Liu realized that traditional log management was wholly inadequate for addressing data sprawl in the modern, cloud-native development stack.

In the first post of our three-part Amazon Redshift series, we covered what Redshift is and how it works. For the second installment, we’ll discuss how Amazon Redshift queries are analyzed and monitored. Before we go deep into gauging query performance on Redshift, let’s take a quick refresher on what Amazon Redshift is and what it does.

If you’ve been writing code for any reasonable amount of time, then it’s virtually impossible that you haven’t handled logging in any way, since it’s one of the most essential parts of modern, “real life” app development. If you’re a .NET developer, then you’ve probably used some of the many famous logging frameworks available for use at this platform. Today’s post will cover one of these frameworks: log4net.

While our engineering team was hard at work improving the performance of the new event viewer, our design team got to work rejuvenating the user experience. We’re excited to offer some serious upgrades and customization options. We organized our interface elements, cleaned up the mobile experience, and adopted a new header and navigation to provide a smoother transition when switching between other SolarWinds® APM products. And of course, we updated the look and feel.

IBM Cloud Log Analysis with LogDNA enables you to quickly find the source of issues and gain deeper insight into application and cloud environment data. IBM Cloud logging begins with log aggregation from application and services within IBM Cloud. IBM partners with LogDNA to bring collection, log tailing and blazing fast log search. LogDNA supports integrations to many cloud-native runtimes and environments.