Operations | Monitoring | ITSM | DevOps | Cloud

Two years ago I wrote a piece in The New Stack about the Future of Ops Careers. Towards the end, I wrote: I described the second category as “operations engineering minus the infrastructure,” dedicated to evaluating and assembling a production stack of third-party platform providers, enabling software engineers to self-serve their services and own their own code in production. I said: That second category I was describing now has a name. We call those teams "platform engineering.".

There is a large sum of assets and medical supply of which the healthcare industry has to keep a track of. Check-ups that are regular to surgeries and the supplies used in it, there is a long-list to it and it would lead to an employee investment of time, plus this technique is also open to human error. An organization always wants to keep good track and manage their supplies in a very effective way.

UC Today, the leading online publication for Unified Communications and Collaboration technology, recently did a deep dive on Martello’s Microsoft-recommended Vantage DX solution for monitoring Microsoft Teams and found out that delivering exceptional user experiences and avoiding a Microsoft Teams outage can be easy with the right solution in place.

Exceptions are the outcomes you do not usually expect in your application. But as a developer, expecting the unexpected is essential to capture exceptions and handle them appropriately. Exception handling is not only applicable to web development projects but also to Unity applications. This article brings everything you need to know as a beginner to Unity exception handling, including methods to handle exceptions, when to use them, and how to manage exceptions easier using distributed logging.

The following guest post addresses how to improve your services’s performance with Sentry and other application profilers for Python. Check out this post to learn more about application profiling and Sentry’s upcoming mobile application profiling offering. We’re making intentional investments in performance monitoring to make sure we give you all the context to help you solve what’s urgent faster.

A typical bit of feedback I have had during my time at Splunk is that the Splunk Machine Learning Toolkit (MLTK) looks nice and all, but how are we supposed to get started using it? Choosing the right technique, let alone the right algorithm can be a daunting task for those who are unfamiliar with machine learning (ML). We’ve been thinking long and hard about how we can help offer more prescriptive introductions into using ML at Splunk and I’m pleased to present our set of MLTK deep dives.

MongoDB is a cross-platform NoSQL database that uses JSON-like documents with optional schema to store data. It was designed for high availability, high performance for high-data persistance use cases, and automatic scaling. Of course, all with the right infrastructure in mind. It is usually a good choice for document-oriented use cases when you need quick prototyping or massive scale. With the massive scale comes massive traffic, though.

Maximizing the value of streaming data requires carefully navigating operational tradeoffs when developing and managing cloud native applications. Organizations that are rapidly producing and processing high volumes of data — like Netflix, Salesforce, Shopify and even the United States Postal Service (USPS), are constantly applying and testing new methods to manage the complexity of data streaming in the cloud.