Monitoring Azure Service Bus (SB) comes with its own set of challenges, primarily due to the distributed nature of the service and the complexities involved in message processing and delivery. Some of the most common challenges associated with monitoring Azure SB include: Message Flow Monitoring: Tracking the flow of messages through various queues or topics, including understanding where bottlenecks might occur or where messages might be delayed.

When an organization attempts to reduce costs immediately, one of its go-to options is to look into the Azure advisor recommendations and take action on them. However, the biggest challenge for many customers is that they often do not know there are saving insights available to take advantage of and reduce thousands of dollars per year in Azure bills. This is because of a few reasons, as defined below;

Last week in Paris, at KubeCon EU 2024, platform engineering was the talk of the event. The topic had a full-day co-located event and also a dedicated track during KubeCon itself. Here’s what I’ve learned from sitting in keynote rooms and then standing at the Spot and NetApp booth till my knees hurt. (Spoiler: It was well worth it.)

There’s a common misconception about running workloads in the cloud: the cloud provider is responsible for reliability. After all, they’re hosting the infrastructure, services, and APIs. That leaves little else for their customers to manage, other than the workloads themselves…right?

Google Cloud provides a wide range of services and tools to help engineering teams reduce the complexity of migrating and deploying applications in the cloud. As engineering teams work to improve the performance, reliability, and security of their applications, they also need to be conscious of cloud costs. But engineers often don’t have access to cost data, or they only see cost data in monthly reports.

Over the past decade, the cloud computing landscape has transformed remarkably, evolving from a nascent technology into a fundamental component of modern digital infrastructure. As the technology has evolved, so too has the need to develop complex cost optimization strategies. A decade ago, businesses grappled with the complexities of brand-new cloud services, often facing challenges in managing costs due to opaque pricing models and a limited understanding of cloud economics.

As of 2023, 89% of companies rely on a multi-cloud approach. Operating in the cloud is no longer a plus but a competitive necessity. Migrating from a fully on-prem to a hybrid or fully cloud environment isn’t exactly easy though, especially given the impenetrability of cloud data. Cloud monitoring enables your company to be proactive about its cloud services, ensuring that availability, security, performance, and other aspects are all up to par before reaching the end-user.

While many of us have already heard of Kubernetes, or may even be leveraging it within our technology stacks, it’s still important to remember that the platform is undergoing massive adoption and evolvement. Due to its relative infancy, Kubernetes is ripe to allow for integrating new technologies, like Artificial Intelligence (AI) and Machine Learning (ML). As an open-source platform, Kubernetes orchestrates containerized applications, ensuring they run efficiently and resiliently.

As businesses increasingly move their workloads to the cloud, managing cloud costs has become a critical concern. At Tidal.Cloud, with the roll-out of new features such as Tidal Accelerator’s Business Analytics, we recently realized that our AWS costs were steadily increasing, and we needed to take action to optimize our cloud spending.