With networks continually scaling to meet business needs, advanced IP scanning has become a crucial task for network admins to ensure network availability and performance. Scanning IP addresses can be challenging in large networks, which often include wired and wireless devices with dynamic IPs, multiple DNS, DHCP configurations, and complex subnets.
The ITOM podcast returns with yet another episode aimed to alleviate all your remote work woes. In the last episode, we drilled into the nuances of VPN security, encryption, hashing algorithms, certification authorities, and much more. Now we’ll discuss the practical implications of VPNs on remote work. VPNs have become many organizations’ best bet to help maintain business continuity in this era of remote work.
The Configuration Management Database (CMDB) has always been a tricky beast to tame, despite having a longstanding presence in enterprise IT. Originally implemented to help track and manage ever-changing IT assets, the CMDB has a checkered past but is finally ready to shed its bad reputation thanks to new technology.
A few months ago, I wrote an in-depth article describing how labels work in Loki. Here, I’m consolidating that information into a more digestible “cheat sheet.” There are some big differences in how Loki works compared to other logging systems which require a different way of thinking. This is my attempt to convey those differences as well as map out our thought process behind them. As a Loki user or operator, your goal should be to use the fewest labels possible to store your logs.
If Sentry were a TV show, I think it would be Lassie. It’s your application’s best friend and everyone can understand it no matter what language they speak. Sentry gets help from the right people to make sure Timmy, I mean your application, is safe and sound. Over the past few months, we improved our Native SDK significantly. Most notably, we increased platform compatibility through a major rewrite from C++ to C and by switching to the CMake build system.
Metrics help you understand how your business and applications are performing. Longer metric retention enables quarter-over-quarter or year-over-year analysis and reporting, forecasting seasonal trends, retention for compliance, and much more. We recently announced the general availability (GA) of extended metric retention for custom and Prometheus metrics in Cloud Monitoring, increasing retention from 6 weeks to 24 months. Extended retention for custom and Prometheus metrics is enabled by default.
This is part two of our journey towards monitoring Azure resources with SCOM, using the Azure Management pack. In Part 1: Installation, we finished installing the Management Pack and managed to connect one of our subscriptions where the resources we want to monitor are. Now, let’s move on to configuring the monitoring for actual resources or services.
An infrastructure, especially if it is scalable, can become extremely complex to visualize and observe. If something goes wrong, it would be difficult to fully understand the problem without a great data monitoring strategy. Information related to CPU, RAM, and statistics about SSH or HTTP servers are critical to understanding the performance of your web-application.
There are tons of query languages. Yet, another query language was invented: the StackState Query Language, or STQL for short. Perhaps this raises some questions. Such as: Why did we not choose to implement SQL? Did we reinvent the wheel? How did we balance the complexity of the language against the time to implement the language? What's the learning curve of this new language? Let me share with you our novel approach.
