Computing

Extending Azure Functions with custom handlers

Another benefit of the hyperscale clouds is their operational expertise and their infrastructure design. They can spin up a new server instance in seconds, providing compute on demand. And with container technologies, they’re able to quickly switch in new isolated environments and scale them up as necessary.

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That’s where serverless technologies come in, building on those skills and the processes to rapidly launch small, stateless processes as necessary. Like much cloud-native development, these serverless processes are event driven, responding to messages, processing their contents, and passing

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We need a new way to think about open source

Open source is about community, so why can’t we get along? I’m referring to the recent fracas between Elastic and AWS, but we’ve dealt with similar issues for many years.

In this most recent episode of Open Source Wars, Elastic accused AWS of “behavior [that] is inconsistent with the norms and values that are especially important in the open source ecosystem.” AWS responded that, actually, it’s Elastic that is violating open source norms and values, leading AWS to fork Elasticsearch and Kibana to “foster healthy and sustainable open source practices—including implementing shared project governance with a community of contributors.”

Personally,

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Big changes are coming for 5G edge computing

According to the latest worldwide market study by Juniper Research, telecom network operator spending on MEC (multi-access edge computing) will increase from $2.7 billion in 2020 to $8.3 billion in 2025. You read that right: Spending will almost triple within the next four years.

MEC (formerly known as mobile edge computing) is a communications approach that enables cloud computing capabilities at the edge of a cellular network, or really any network. 

[ Also on InfoWorld: Amazon, Google, and Microsoft take their clouds to the edge ]

As the emerging 5G (fifth generation) network systems continue to roll out during

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Using OPA with GitOps to speed cloud-native development

One risk in deploying fleets of powerful and flexible clusters on constantly changing infrastructure like Kubernetes is that mistakes happen. Even minute manual errors that slip past review can have substantial impacts on the health and security of your clusters. Such mistakes, in the form of misconfigurations, are reportedly the leading cause of cloud breaches, for example. And, with everything that can happen in the containerized world, these types of mistakes are virtually guaranteed to occur.

The question, then, is how developers and platform engineers can, under today’s accelerated development timeframes, minimize these errors — if not eliminate them entirely

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3 cloud architecture patterns that optimize scalability and cost

The two most mentioned advantages of cloud-based platforms are pay-per-use billing and the ability to scale up to an almost unlimited number of resources. No more buying ahead of resource demand, attempting to guess the amount of physical hardware and software that you’ll need.

But enterprise IT needs to understand that scale and costs are coupled concepts in cloud computing. The more resources you use, either self-scaling or auto-scaling, the more you pay. How much you pay may depend on the architecture patterns as much as on the cost of the resources themselves. Here’s why.

In building cloud-based systems, I’ve

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Google Cloud named best performing cloud for 2021

Google Cloud Platform (GCP) is the most performant public cloud infrastructure-as-a-service (IaaS) provider for running online transactional processing (OLTP) workloads, but Amazon Web Services (AWS) remains the best value for the money.

That’s according to the 2021 Cloud Report from Cockroach Labs, the company behind the open source CockroachDB database, which recently raised a $160 million mega-round of funding.

“Declaring a winner was much harder to declare than in years past,” according to the authors of the report, as the gap on most metrics was razor thin.

The researchers benchmarked each of the major providers according to 54 machine configurations

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