[Infoblog] Kubernetes for Storage
Kubernetes for storage is useful for storage administrator because it enables different forms of persistent, stateful data retention within Kubernetes clusters deployment, which are increasingly popular.
Demystifying Persistent Storage Myths for Stateful Workloads in Kubernetes
Kubernetes provides workload portability. That is, any workloads should spontaneously run on any type of infrastructure where Kubernetes clusters are deployed. In the case of handling stateful workloads, it may not easy to set up persistent storage but it is not impossible
Simplify Storage for Kubernetes with Rook and Ceph
Kubernetes has enabled applications to be cloud native, but application storage has not been enabled with cloud native features from the start. Rook bridges that big gap by making storage cloud native in conjunction with Ceph and other storage systems.
Let’s go beyond QA and look at QE
By integrating quality at every step of the software development lifecycle, companies can create products that set new standards of quality in the market. It’s clear that Quality Engineering is now a strategic function with the potential to deliver a competitive advantage.
Analyzing Business Impact of Cloud Modernization
Cloud Modernization may be used in varied sense of its meanings. However for businesses, it means, transforming your infrastructure and applications so that cloud provides best ROI on an ongoing basis. Using cloud modernization to unlock the value of your applications and data, by synergizing them with modern tools of analytics and AI.
[Infoblog] Container Attached Storage
At a high-level Container Attached Storage is software that includes microservice based storage controllers that are orchestrated by Kubernetes. These storage controllers can run anywhere that Kubernetes can run which means any cloud or even bare metal servers or on top of a traditional shared storage system.
Will Blockchain disrupt the world of data storage as we know it?
Blockchain-enabled data storage seems to provide the advantages described above. The question is, in the context of the Enterprise how long will it be before business leaders consider it proven enough to dive in?
Storage Analytics is becoming more complex – can AI and ML help?
Physical servers and storage equipment are a data center reality. How can we ensure that the workloads are distributed correctly across this infrastructure?AI and Machine Learning can come to the rescue here as well. With these technologies, data centers can distribute workloads equally and efficiently across these servers.
A Honest Look – Can AI Help Us Build Better Software Products?
The software development industry sure has changed. We used “waterfall” to build software products in a phased, sequential manner. Then the Software Development Life Cycle started becoming less relevant. From Agile and DevOps to automated testing, and mobile and cloud technology –disruption hit the software development process.
The Problem with Enterprise Data Storage and One Promising Solution
With the new information generated per second for every human being predicted to hit 1.7 megabytes by 2020, the expectations from data are enormous. The ability to analyze critical data and unearth critical insights can take enterprises to an entirely different level.