The benefits of containerizing workloads are numerous and proven. But, during infrastructure transformations, organizations are experiencing common, consistent challenges that interfere with accurately forecasting the costs for hosting workloads in Kubernetes. Planning the proper reservations for CPU and memory before migrating to containers is a persistent issue Densify observes across our customers.

To have full visibility into modern cloud environments, businesses need to collect an ever-growing avalanche of log data from a range of highly complex data sources. Indexing logs is key for real-time monitoring and troubleshooting, but it can quickly become expensive at high volumes, meaning that organizations often must choose which logs to index and which to archive.

When you’re planning an application performance monitoring (APM) strategy, collecting metrics from storage services like Amazon S3 may not seem like a priority. After all, part of the point of object storage is that applications can read and write from storage buckets seamlessly, with minimal configuration and overhead. Unlike databases or file systems, storage buckets don’t require complex configurations that could lead to performance issues.

As organizations move their workloads into the public cloud, their environments gradually evolve. Initially, an organization tends to aim for a 1:1 migration of resources, typically in the form of virtual machines (VMs). Although this approach allows for an easier data migration path, it’s more costly and offers less flexibility than taking advantage of some of the serverless and Platform-as-a-Service (PaaS) offerings in the public cloud.

The AWS Elastic Load Balancing (ELB) automatically distributes your incoming application traffic across multiple targets, such as EC2 instances, containers, and IP addresses, in one or more Availability Zones, ultimately increasing the availability and fault tolerance of your applications. In other words, ELB, as its name implies, is responsible for distributing frontend traffic to backend servers in a balanced manner.

The evolution of Software Engineering over the last decade has lead to the emergence of numerous job roles. So how different is a Software Engineer, DevOps Engineer, Site Reliability Engineer and a Cloud Engineer from each other? In this blog, we drill down and compare the differences between these roles and their functions.

Cloud teams are busy. In fact, Virtana’s recently published State of Hybrid Cloud and FinOps survey found that 44% of respondents have deployed more than half of their workloads in a public cloud, and 88% have deployed more than one-quarter of their workloads in a public cloud. That’s a lot of migration, optimization, and management on the cloud team’s plate; and it’s not just for right now but for the foreseeable future.

Managing technology isn't your only responsibility as a CTO. You could be the CIO, Digital Enabler, Data Officer, and Engineering Leader all rolled up into one, whether you work at a startup, scaleup, or enterprise. Your role as a C-suite executive for engineering requires you to research and implement the newest, most efficient technologies. As CTO, you’re responsible for: The list goes on and on. That's why you, of all people, need the right tools to manage your vital role in the digital world.

We're pleased to announce that our customers can now turn on full-stack observability across their services and workloads deployed on Azure Spring Cloud.

At Speedscale, we are on the cutting edge of defining autonomous testing for the cloud era. However, we aren’t the only company trying to solve this problem and we enjoy learning from every perspective. That’s why Facebook’s recent blog article about autonomous testing caught my eye. They’ve built a sophisticated autonomous test system that introduces many of the same techniques we utilize.