Operations | Monitoring | ITSM | DevOps | Cloud

Some of you may have attended the recent webinar on how to simplify ticket remediation with ML-Powered Analysis. We’re thrilled to announce that we have packaged the new app shown in that demo – the Smart Ticket Insights App for Splunk – and it is now live on Splunkbase!

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.

With the new Streama capability announced today, you no longer have to choose what to monitor and what to drop to manage your logging costs. For years, our customers have enjoyed the benefits of a log analytics platform that enables them to autonomously manage and analyze data in their cloud applications. Our machine learning engine empowers users to improve their system stability and accelerate their release cycles.

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).