As companies continue to invest, adopt, and grow into cloud, as well as manage a strong public and hybrid cloud environment, it is very important to understand what are the common pain points of their sub-organizations.
Setting and tracking appropriate target metrics are an important part of a product manager’s job. Goals must be defined, not just as inspiring vision statements, but also as quantifiable targets that can be objectively measured. Metrics can be deployed in different contexts and for purposes; however, useful metrics in one scenario can be misleading in regards to another. The problem comes when you’re not clear about what kind of metric you’re trying to set.
This blog was co-written by Howard Twine and Gregory Green. A few years ago, a colleague of ours wrote an informative post to help readers understand when to use RabbitMQ and when to use Apache Kafka. While the two solutions take very different approaches architecturally and can solve different problems, many people find themselves comparing them for situations where there is overlap.
A couple of weeks ago, I wrote about The Art of True Chargebacks and how VMware Tanzu CloudHealth makes simple work of the cost reallocation of cloud expenses. In addition to being a refresher on cost reallocation with Tanzu CloudHealth, it was also shared as a kickoff of our newly released cutting-edge Cost Reallocation API. This officially available API revolutionizes the seamless automation of chargeback reallocation across diverse geographies, brands, business units (BUs), and product lines.
KuppingerCole AG published its report assessing Cloud Security Posture Management (CSPM) solutions in the market for 2023. Their leadership compass helps cloud users find an appropriate solution to meet CSPM needs of an organization to monitor, assess, and manage risks associated with the use of cloud services. Fifteen vendors were assessed based on responses to a questionnaire, strategy briefing, and demo.
Cloud computing and AI/machine learning (ML) are two powerful technologies that are even more impactful when used together. Cloud computing provides the infrastructure and resources needed to support AI/ML applications; while AI/ML enhances cloud computing by providing intelligent automation and decision-making capabilities.
Many organizations struggle with managing thousands of services and applications. A typical environment consists of a combination of modern cloud applications, on-premises workloads, and workloads that are in the process of being moved to the cloud. IT and operations teams can easily be overwhelmed by the large volume of data and activity that is generated across these systems.
VMware is glad to share that we have been named a Leader in the inaugural Gartner® Magic Quadrant™ for Container Management.
