Operations | Monitoring | ITSM | DevOps | Cloud

We're excited to announce that starting with the new Splunk Cloud Product 9.0.2205 release, it's easier to create, manage and use private apps. Although Splunk is great by itself, we can all agree that the real value of Splunk comes from all the applications that Developers, SplunkTrust folks and Splunkers build.

Most classical, batch-oriented machine learning systems follow the paradigm of “fit and apply”. In an earlier blog post, I discussed a few patterns on how to better organize data pipelines and machine learning workflows in Splunk. In this blog, we’ll review how you can organize your machine learning model in a new way: online learning.

A goal of open-source observability is unifying several different signals to provide the observability everyone wants. It’s always interesting to speak to people on this journey, and how they try to provide it through open-source projects, and the challenges they can face. I was thrilled to host Pranay Prateek on the most recent episode of the OpenObservability Talks podcast.

Have you ever tried to find a bug in a multi-layered architecture? Although this might sound like a simple enough task, it can quickly become a nightmare if the system doesn’t have proper monitoring. And the more distributed your system is, the more complex it becomes to analyze the root cause of a problem. That’s precisely why observability is key in distributed systems. Observability can be thought of as the advanced version of application monitoring.

Just about every organization today relies on key applications running on complex multi-cloud environments to transact business and enable users to work. It is critical to ensure that those applications are running optimally. A solid monitoring and alerting system is required to know when an issue needs attention. But having a robust monitoring system is not enough.

A reliable digital customer experience is critical to the success of digital-first businesses. Each minute of downtime can result in the loss of revenue, unsatisfied customers, and damage to reputation. However, as your uptime gets closer to 100%, it gets exponentially harder to improve and often comes at the cost of speed of innovation. A good balance between innovation (i.e., new feature releases) and maintaining an acceptable level of reliability is key to success in the digital world.

Almost every organization is going through digital transformation. According to IDC, direct digital transformation investment is growing globally at a compound annual growth rate of 15.5% and is expected to approach $6.8 trillion by 2023. Customers quickly embrace the benefits of a customer experience reshaped by technology. However, they have little patience when that technology doesn’t work as expected.

Monitoring the digital experience of users is a must-have these days. Ensuring the end clients are satisfied is difficult though. People are not keen to provide feedback; they just change the vendor without explanation. It is, therefore, crucial to build enough observability into a front-end application (a web page or a mobile UI) so it can tell the story of how well the user perceived the interaction within the application.

At this point, you already know how powerful syslog is (and if you don’t, check out “Introduction to Syslog”). But here’s the thing: Scaling your systems to consume high volume syslog is like fighting zombies. Weird unexpected behavior and no easy solutions. Before you fight zombies, though, you have to understand them. So, here are the challenges for scaling syslog one by one.