AWS Lambda has gained good traction for building applications on AWS. But, is it really the best fit for all use cases? Since its introduction in 2014, Lambda has seen enthusiastic adoption – by startups and enterprises alike. There is no doubt that it marks a significant evolution in cloud computing, leveraging the possibilities of the cloud to offer distinct advantages over a more traditional model such as EC2.
Cloud spend optimization is a challenging endeavor for most companies. The major reason for this is that most don’t have sufficient visibility into their live cloud costs, so they can’t tie day-to-day decision-making to those staggering AWS bills that arrive at the end of each month (way too late to make meaningful changes).
Earlier this month we announced the latest iteration of our award-winning hybrid IT infrastructure management and AIOps platform, VirtualWisdom. This was one of our biggest product-related announcements of the year, and we were thrilled with the response we received not only from our partners and customers, but also from the media and analyst community. Let’s take a look at what some folks had to say about the new VirtualWisdom.
One of the most common reasons for system failures is changes to the underlying infrastructure. Amazon CloudTrail does a great job of recording when actions are taken but a lot of organizations don’t take advantage of it. FireHydrant now includes this data, giving you visibility into changes to your infrastructure while you’re investigating an incident.
Organizations need to continually ramp up and improve their security and resilience to unexpected incidents. But as the number of endpoints, networks, and user interfaces grow exponentially, the task becomes more difficult, and manual incident response management becomes less and less effective.
A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.
BugSplat now includes local variables and function arguments for our Windows Native, Unity, and Unreal C++ integrations. By including local variables and function arguments, we can provide another level of contextual information about the cause of your crash. This new feature may help reduce or eliminate the need to debug a crash report on your local development machine.
The filter navigator sites in the top left modestly heading up the table of options. Until recently I’ve been using it to just filter the list below, then, someone introduced me to a couple of other cool commands you can run. The below six functions are all run against a table, for my examples I’ve uses sys_user, but any table can be used. I was able to find the New York documentation for this functionality here if you’d like more details.
A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.
I can’t remember the exact reason I created this script include, but after finding it figured I’d draft up a couple quick examples as the logic could be expanded to other checks, plus, I might need it again one day so its good to have on hand. The script include detailed below creates a new class type in ServiceNow that can be used both from the client-side with Ajax, and from the server-side as a simple object call.
