Edge computing is getting a great deal of attention now, and for good reason. Cloud architecture requires that some processing be placed closest to the point of data consumption. Think computing systems in your car, industrial robots, and now full-blown connected mini clouds such as Microsoft’s Stack and AWS’s Outpost, certainly all examples of edge computing.

The architectural approach to edge computing—and IoT (Internet of Things), for that matter—is the creation of edge computing replicants in the public clouds. You can think of these as clones of what exists on the edge computing device or platform, allowing you to sync

Nvidia is extending its solution footprint far beyond artificial intelligence (AI) and gaming, venturing broadly across the entire computing ecosystem into mobility and the next-generation cloud data center.

Nvidia’s ambitions in this regard are clear from its pending acquisition of Arm Technology and from CEO Jensen Huang’s positioning of the company as a “full-stack computing” provider. Demonstrating that he’s putting substantial R&D dollars behind this vision, at the virtual Nvidia GPU Technology Conference this month, Huang announced the rollout of the company’s new BlueField “data processing unit (DPU)” chip architecture.

Accelerating diverse workloads through programmable CPU offload

The geographic distribution of cloud data centers makes it easy to configure SQL Server instances for high availability (HA) and/or disaster recovery (DR), but what good is that to you if you’re already invested in a well-tuned SQL Server system that’s firing away on all cylinders on-premises? The idea of ensuring higher levels of availability or easily adding a disaster recovery site may sound very attractive, but the thought of moving everything into the cloud to achieve those ends may give rise to cold sweats and nightmares.

But here’s the thing: The cloud is not an all-or-nothing proposition. You can

Cloud operations, aka cloudops, is the long tail in the cloud computing migration and development story. It takes place after you deploy cloud-based solutions and then operate them over a long period of time. Cloudops determines the success of a migration or development effort and the success of user and customer experiences.

Some things going wrong in cloudops right now need some attention. First is too many types of operational tools, such as management and monitoring. These tools drive more operational complexity, which can result in human errors that, in turn, cause operational issues. Another problem is enterprises that underestimate

Many analytics and machine learning use cases connect to data stored in data warehouses or data lakes, run algorithms on complete data sets or a subset of the data, and compute results on cloud architectures. This approach works well when the data doesn’t change frequently. But what if the data does change frequently?

Today, more businesses need to process data and compute analytics in real-time. IoT drives much of this paradigm shift as data streaming from sensors requires immediate processing and analytics to control downstream systems. Real-time analytics is also important in many industries including healthcare, financial services, manufacturing, and

I drew some big lines in the sand by predicting that the pandemic would cause a run on cloud computing. The pandemic quickly proved that companies leveraging more public cloud resources than their competitors and peers have much less exposure to risk than those that do not. Priorities soon shifted to cloud migration on the fastest path possible. The result is a pandemic run on cloud migration tools, experienced cloud pros, cloud consulting, and public clouds themselves. 

Most of us didn’t believe the experts’ predictions that pandemic quarantines and restrictions could linger until the end of summer, with another