Operations | Monitoring | ITSM | DevOps | Cloud

IDG’s recent “State of the CIO” survey across IT leaders has revealed the impact of COVID-19 on IT organizations and the sudden and unforeseen shifts of their initial 2020 plans.

We're excited to announce that the Splunk Machine Learning Toolkit (MLTK) version 5.2 is available for download today on Splunkbase! Earlier this month, I discussed how the release of version 5.2 will make machine learning more accessible to more users. Splunk’s MLTK lets our customers apply machine learning to the data they're already capturing in Splunk, develop models, and operationalize these algorithms to glean new insights and make more informed decisions.

Computing environments are constantly changing. Back when an on-premises server hosted your work, your infrastructure and applications were easy to track. Now that you’re developing in the cloud, things are more challenging. You’re learning that each team within your organization uses a different monitoring tool. At this point, you may be wondering if it’s time to build your own monitoring solution with open source tools at its core that everyone can use.

Here at Sumo Logic, we share a lot of thoughts about managing data at scale, and the innovative ways we help customers address their unique use cases. It’s not just about analysis of logs. In this article, I will talk about another important observability signal: distributed traces. I will share a few observations about how we at Sumo think about the future of adoption of distributed traces, a very important concept, taking from our own experience.

Everyone at Splunk is very proud of the amazing things that our customers and partners do with their data. It is always extra special when one of those organisations is really doing good and looking after us all in our daily lives. I’m delighted to share one of those stories from the Derbyshire Fire & Rescue Service (DFRS) who is using Splunk as its data-driven SIEM.

One of the great things about developing for Splunk Security Essentials is that most of the features and capabilities are requested from customers and the security community. In this latest release (3.1), we added a feature that has been requested frequently: the ability to filter the ATT&CK Matrix for Cloud and SaaS Techniques. The MITRE ATT&CK Framework consists of multiple matrices such as Enterprise, Mobile, and ICS.

This guide is focused on how to log in Python using the built-in support for logging. It introduces various concepts that are relevant to understanding Python logging, discusses the corresponding logging APIs in Python and how to use them, and presents best practices and performance considerations for using these APIs.

Today, continuous development and continuous delivery have become the de facto mode of application development for businesses seeking higher agility. With the continuous push to achieve higher efficiency and reliability in running software operations, container technologies like Docker, Kubernetes, and Mesos have become mainstream. Among these technologies, Docker is the most popular.

Logging is an essential method to understanding what’s happening in your environment. Logs help developers and system administrators understand where and when things have gone wrong. Ideally, logs on their own would suffice as indicators of what’s happening. However, there’s far too many log messages being produced in today’s world and most don’t contain the information we actually need.