(This is the first post by our new head of Customer Success, Irving.) Sampling is a must for applications at scale; it’s a technique for reducing the burden on your infrastructure and telemetry systems by only keeping data on a statistical sample of requests rather than 100% of requests. Large systems may produce large volumes of similar requests which can be de-duplicated.
You’ve been staring at your screen for hours, trying to check why a certain bug is occurring only for end-users in your production environment. You’ve tried a multitude of approaches: adding log lines in all kinds of indicative places, logging potentially relevant variables to get an indication of the state in which the bug occurs, and the list just goes on. Sounds familiar?
LEHI, Utah, Jan. 16, 2020 — SaltStack®, the creators of intelligent automation software for security operations teams, today announced SaltStack SecOps products were recognized as the Best SecOps Security Solution in the 2020 Tech Ascension Awards, and as a Silver Winner in the Security Software category of the One Planet Awards.
CIO Dive is one of the leading providers of news and analysis for IT executives. To receive a shoutout from them is always great. Just recently, CIO Dive published an article outlining how Opsani successfully optimized cloud use at Ancestry – without hampering user experience.
When it comes to setting quotas, a lot of sales managers like to turn to a simple annual run rate. And while an ARR may be the quickest and easiest way to predict future sales results, we’ve seen that it’s far from the most precise metric. By contrast, the exponential smoothing formula lies on the complete opposite end of the spectrum. Although it’s one of the most complicated sales forecasting methods, it is also arguably the most accurate.
InfluxData prides itself on prioritizing developer happiness. A large part of maintaining developer happiness is providing client libraries that allow users to interact with the database through the language and library of their choosing. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.
How you build your application absolutely impacts the lives of those in charge of supporting it. This isn’t a correlation we generally make, but thinking about what happens when things break as you build your application will help everyone. Developers should be thinking about ways they can improve incident management and response through code, especially because more and more developers are on-call. In this post, I’ll explore how bulkhead and sidecar design patterns can do just that.
On the sleepy island of Gozo, security isn’t a concern. Tourists can leave their bags on the beach and go off on an adventure without worrying that their belongings will be stolen. In my home city, however, we say that “if you don’t tie it down, it’s not yours.” Everything can be stolen. Similarly, the internet is the biggest and busiest city in the world! If it can be read, copied, written, or injected with SQL, it’s not yours.
As the open-source monitoring system Prometheus grew, so did the need to grow its capacity in a way that is multi-tenant and horizontally-scalable, along with the ability to handle infinite amounts of long-term storage. So in 2016, Julius Volz and Tom Wilkie (who is now at Grafana Labs) started Project Frankenstein, which was eventually renamed Cortex.
When it comes to managing your work, there are dozens and dozens of project management methodologies to choose from. But as you begin your research into which methodology is right for you, you’re probably going to see one particular word show up over and over again: Agile. It seems to shimmer in your peripheral vision like some sort of project management mirage. Is it real? Can all the avowed benefits of agile project management really be true?
Even though containers have been around for ages, it wasn’t until Docker showed up that containers really became widely adopted. Docker has made it easier, faster, and cheaper to deploy containerized applications. However, organizations that adopt container orchestration tools for application deployment face new maintenance challenges.
In Part 1 we’ve described what container monitoring is and why you need it. Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and the applications they run. These servers and applications running on them are typically very static, with very long uptimes.
