Serverless first came onto the scene in 2014 when AWS Lambda was launched. It offers a dynamic cloud-computing execution model where the server is run by the cloud provider. As with any relatively recent technology, its novelty results in a steep learning curve, and it comes with its own set of benefits and drawbacks.
Python has rapidly grown into one of the most popular languages for automation. That’s why LogicMonitor supports Python when it comes to managing your LM portal and expanding your monitoring coverage with scripts. During your scripting adventures, you may find yourself wanting to take your work to the next level. One widely-applicable improvement is credential abstraction.
Kubernetes clusters use multiple certificates to provide both encryption of traffic to the Kubernetes components as well as authentication of these requests. These certificates are auto-generated for clusters launched by Rancher and also clusters launched by the Rancher Kubernetes Engine (RKE) CLI.
Unless there’s been a huge breakthrough in technology that proves beneficial to employees, or something goes wrong that affects the business’ typical work processes, IT teams’ efforts often go unnoticed. Since significant breakthroughs are few and far between, more often than not, when someone’s contacting IT, it’s with a problem.
Logs Monitoring plays an important role in analyzing, troubleshooting and alerting on problems. Organizations have lots of data in logs that should be mined to get valuable insights on users, applications and systems behavior. Real time alerting from logs monitoring does help in identifying the problems early. It also provides security related insights when someone is trying to break into your organization.
It is no longer sufficient for IT operations teams to monitor resources like CPU, memory and disk utilization. The success of any IT initiative these days is measured based on user experience. Irrespective of the type of application in question – whether email, Citrix/VDI, SAP, web application – application availability and response time are key measures of user experience. Application outages not only affect users, they can also impact the business.
It’s five o’clock on a Friday afternoon. There are no new bug reports and everything is looking smooth. Your plan of a relaxing weekend is in sight when you get a call—the website you look after isn’t responding. Yikes. AWS Lambda minimizes the chance of this truly terrifying event from happening by taking care of server maintenance while you focus on coding robust applications.
When gaming online, your “ping” refers to the time it takes for any input from your mouse, keyboard, or controller to register with the server that is hosting your game. Ping rate is measured in milliseconds and anything below or around 20ms is ideal, however, even slight increases above this can result in noticeable differences. Even with a ping rate of around 100ms, games are still playable, but where you will start seeing significant delays is around 150ms.
As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.
Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.
