Operations | Monitoring | ITSM | DevOps | Cloud

The default pod provisioning mechanism in Kubernetes has a substantial attack surface, making it susceptible to malevolent exploits and container breakouts. To achieve effective runtime security, your containerized workloads in Kubernetes require multi-layer process monitoring within the container.

Migrating your workloads to the cloud can bring some undeniable benefits to your organisation. For example, you can leverage cloud automation to significantly improve your time to market. You can also benefit from the ever increasing number of cloud regions to place your workloads close to your clients. This improves the response time of your services and, as a result, your customers’ satisfaction.

The MongoDB document-oriented database is one of the most popular database tools available today. Developed as an open-source project, MongoDB is highly scalable and can be set up in your environment in just a few simple steps. When running and managing databases, monitoring is a key requirement.

Content As we already discussed in previous articles, the Kubernetes control plane is made up of a few key components playing different roles. Those are necessary to ensure both Kubernetes and applications are functional and behaving properly.

The current state of the economy has left many companies in a state of uncertainty, with 58% of business leaders feeling the pressure of too many initiatives to be supported without the right resources. While these companies look for ways to stay competitive and, in many instances, to accomplish more with less, compromising on delivering high-quality experiences is not an option, particularly in a world where fast, seamless digital experiences have become the norm.

Machine learning affects nearly every aspect of our daily lives. To understand how this technology works and how you can use machine learning, it’s necessary to know the difference between unsupervised and supervised machine learning. The following are essential points regarding the different aspects of unsupervised and supervised machine learning.

Push came to shove with disruption and innovation during the pandemic. Enterprises started leveraging new technologies to gain competitive advantage, enter new markets, support employees from home and maintain business continuity. As a result, IT is faced with new challenges that are critical for realizing the value of the digital transformation that started and accelerated during the pandemic. To state the obvious, CIOs must manage an IT infrastructure that’s safe, sound and secure.

The outages span the giants of the Internet and some of the biggest failures of IT resilience we were subject to – from AWS’s trifecta of outages in December 2021 to the October ‘21 outage that took down Facebook, Instagram, WhatsApp, and interrelated services. We also look at some more intermittent outages that you may have missed.

You’ve probably heard the phrase “transparency is key” more than you can bear at this point—so let’s get this out of the way. Transparency is key. The phrase suddenly became that much more unbearable. But before you drop off, let me also communicate something else: transparency is often not enough. Often, companies make the mistake of leaning on transparency as a catchall solution to many of their internal comms issues.

Today, businesses are generating more data than ever before. However, with this data explosion comes a new set of challenges, including increased complexity, higher costs, and difficulty extracting value. With this in mind, how can organizations effectively manage this data to extract value and solve the challenges of the modern data stack?