Operations | Monitoring | ITSM | DevOps | Cloud

As companies responded to the COVID-19 pandemic with remote work, cybercriminals increased their social engineering and ransomware attack methodologies. Ransomware, malicious code that automatically downloads to a user’s device and locks it from further use, has been rampant since the beginning of March 2020. According to a 2020 report by Bitdefender, ransomware attacks increased by seven times when compared year-over-year to 2019.

The abundance of programming languages available today gives programmers plenty of tools with which to build applications. Whether long-established giants like Java or newcomers like Go, applications need monitoring after deployment. In this article, you will learn how to ship Golang logs to the ELK Stack and Logz.io. It’s usually possible to get an idea of what an application is doing by looking at its logs. However, log data has a tendency to grow exponentially over time.

Logging has been around since programming began. We use logs to debug issues and understand how software works at the code level. After logging and debuggers, profilers are a dev’s best friend when writing code and may run in production with limits to reduce overhead. As we distributed architectures — making systems more complex — centralized log aggregation was soon necessary. At that point, we had to analyze this data. Hence, log analytics technologies were born.

Many organizations aspire to become true, high-functioning DevOps shops, but it can be hard to know where you stand. According to DevOps Research and Assessment, or DORA, you can prioritize just four metrics to measure the effectiveness of your DevOps organization—two to measure speed, and two to measure stability.

We’ve seen quite a bit of change this year as businesses have had to pivot to accelerating their digital transformation strategy, and placing even more emphasis on leveraging technology as a competitive differentiator. Most have continued to stress the importance of maintaining excellent customer relationships through their contact centers, but the playing field has changed as they now have to tap into data for insights that may have normally been gleaned through an analog approach.

Cloud Profiler is a favorite of Google Cloud customers thanks to the insight that it provides into the performance of your production code. You can use this knowledge to reduce and shorten outages, improve performance, and optimize compute spend—always a popular topic! Profiler has always provided the ability to view and compare CPU and memory performance over time through time filters and the comparison feature.

Logstash has a number of helpful plugins. We’ve covered the mutate plugin in great detail here, as well as Logstash grok, but it was time to go over some of the others. Here, the Logstash Prune Filter will get its due attention. Its existence owes to the need to remove fields according to select blacklists or whitelists of field names and their associated values. Put more curtly, it prunes the excess branches (fields) in your garden (your data).

Running an ELK stack provides unrivaled benefits for your organization, however, ELK issues will inevitably crop up. ELK is scalable, and largely agnostic of internal infrastructure, making it a great asset for SMEs and enterprises. However, successfully deploying and running an ELK stack is not without its difficulties. In order to keep your ELK stack running at optimum performance, you need to familiarize yourself with some of the most common ELK issues.

In Cloud Logging, we understand that logging is a critical part of what it takes for you to operate reliable applications and infrastructure on Google Cloud. We’ve added new features to help you more easily store, find and control your logs. Today, we’re announcing a new default logging experience: Logs Explorer. Previously known as Logs Viewer Preview, Logs Explorer provides new tools for you to better understand and analyze your logs during the troubleshooting process.