I remember a discussion with a group of friends around 8 years back. Microsoft was in it’s early days of becoming a leader in the cloud. Those friends, all techies in the Seattle area had varying expectation on how it would work out. Many thought that a full blown move was few decades away because their experience indicated that all big companies ran on stack that was very old and simply couldn’t be moved to the cloud any time soon.
Waves of workloads have been since moving to the cloud. A new brew of startups were cloud native from the start and they were the first to use the power of cloud. Many large and small enterprises had already virtualized workloads and they moved as well. Some moved their new workloads (green-field), some even followed lift-n-shift with some modifications (brown-field) into the cloud.
However, a class of large enterprises were stuck in their data centers. They wanted to use the power of the cloud, they wanted to use IoT integration, Machine-learning and the capability of elastic growth of their applications, but the center of their systems were running on some stack that did not run in the standard virtualization offered in the cloud. These enterprises said that if they cannot move those workloads into the cloud, they would need to keep the lights on in their data-centers and moving some peripheral workloads simply did not make sense.
This is where Azure Dedicated and we come into the picture.
Waves of workloads have been since moving to the cloud. A new brew of startups were cloud native from the start and they were the first to use the power of cloud. Many large and small enterprises had already virtualized workloads and they moved as well. Some moved their new workloads (green-field), some even followed lift-n-shift with some modifications (brown-field) into the cloud.
However, a class of large enterprises were stuck in their data centers. They wanted to use the power of the cloud, they wanted to use IoT integration, Machine-learning and the capability of elastic growth of their applications, but the center of their systems were running on some stack that did not run in the standard virtualization offered in the cloud. These enterprises said that if they cannot move those workloads into the cloud, they would need to keep the lights on in their data-centers and moving some peripheral workloads simply did not make sense.
This is where Azure Dedicated and we come into the picture.
SAP HANA Large Instance
For some of these customers that #$%#@ is SAP HANA in-memory DB on a single machine with 768 vCPUs and 24 terabytes of ram (yup) and we have them covered. Some wanted to scale those out to 60 terabytes in memory, we have them covered too with our bare-metal machines running in Azure. See SAP HANA Large Instances on Azure.We started our journey in this area with this workload. Now we have evolved into our own little organization in Azure called Azure Dedicated and also support the following workloads.
Azure VMware Solutions
Some customers wanted to run their VMware workloads and we have Azure VMware Solution for them.
Hardware Security Modules
In partnership with other teams in Azure we support HSM, which are standard cryptographic appliances powering say financial institutions.Cray Supercomputer?
So you need to simulate something or do ML on tens of thousands of cores, we have Cray super computers running inside Azure for that!!Azure NetApp Files
Working closely with the storage team we deliver demanding file based workloads running on Azure NetApp filesWhat next?
As an organization we are always on the lookout for more exciting opportunities to bring the power of cloud to our customers, wherever they are. So keep looking out for new announcements from our org.How do we support these workloads?
Our team builds cutting edge, highly available, scalable, distributed systems to power these workloads in Azure. Our engineering stack uses linux based containers on Kubernetes, AKS, Helm, linkerd, fluentd, docker, go-lang, python, .netcore, C#, F# etc. Some samples of how we build and run our systems
