Operations | Monitoring | ITSM | DevOps | Cloud

Over the past few years, cloud computing has passed through its hype and early-adopter phases. Now we are hitting the peak of migration from on-premise to cloud-based infrastructure. Consequently, this transition and the advent of cloud computing has dramatically changed the way we think about security. Namely, the security paradigm has shifted towards a Zero Trust Security Model.

Graylog’s log aggregation features are useful for a lot of tasks, ranging from regular troubleshooting to detecting issues as soon as they become manifest. Optimizing log management by aggregating all meaningful data is a quick and efficient way to isolate any problem to root causes and solve it with minimal impact on services. Aggregated data is easier to parse and analyze – you can reduce the number of data points in a meaningful way and obtain the answer you need from them.

Since we released Loki, our log aggregation system, our customers have been asking about using it not only for logs but also for less frequent events. Even though the strength of Loki lies in its ease of operation and automatic labeling for high-frequency logs, it can also be used for events out of the box.

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.