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Whenever new technologies emerge, the first priority for a technologist is to understand the implication of adopting it. Serverless architecture is a case in point. Unfortunately, much of the current literature around serverless architecture focuses solely on its benefits. Many of the articles —and examples used — are driven by cloud providers —so, unsurprisingly talk up the positives. This write-up attempts to give a better understanding of the traits of serverless architecture.

Serverless is the latest innovation in cloud computing that promises to alter the cost-benefit equation for enterprises. As our CEO, Sid Sijbrandij says, ‘All roads lead to compute.’ There is a race among providers to acquire as many workloads from enterprises as possible, at the cheapest cost. The latter is where serverless comes in: serverless computing is an execution model in which the cloud provider acts as the server, dynamically managing the allocation of machine resources.

It’s been almost a year since support for the Go programming language in AWS Lambda was announced at re:Invent 2017. Meanwhile the Serverless hype train has gained full steam, FaaS offerings of all major cloud providers have reached a respectable level of maturity and an increasing amount of teams are building applications based on the model while figuring out how to best manage software consisting of a set of managed cloud resources and individually deployable functions.

Knative, the open-source framework that provides serverless building blocks for Kubernetes, is on a roll, and GKE serverless add-on, the first commercial Knative offering that we announced this summer, is enjoying strong uptake with our customers. Today, we are announcing that we’ve updated GKE serverless add-on to support Knative 0.2. In addition, today at KubeCon, RedHat, IBM, and SAP announced their own commercial offerings based on Knative. We are excited for this growing ecosystem of products based on Knative.

Scalability is often a key issue for many growing organizations. That’s why many organizations use Apache Kafka, a popular messaging and streaming platform. It is horizontally scalable, cloud-native, and versatile. It can serve as a traditional publish-and-subscribe messaging system, as a streaming platform, or as a distributed state store. Companies around the world use Apache Kafka to build real-time streaming applications, streaming data pipelines, and event-driven architectures. Source: redhat.com

Creating serverless applications is a multi-step process. One of the critical steps in this process is packaging the serverless functions you want to deploy into your FaaS (Function as a Service) platform of choice. Before a function can be deployed it needs two types of dependencies: direct function dependencies and runtime dependencies. Let’s examine these two types. Direct function dependencies– These are objects that are part of the function process itself and include: Runtime function dependencies– This is data related to the runtime aspects of your function.

Firecracker is an eye-catching virtual machine from AWS. It is lightweight, secure, and uses Linux. Forget bloat, minimalism is the future of serverless. Firecracker is a new virtualization technology that enables customers to deploy lightweight micro Virtual Machines or microVMs. Firecracker microVMs combine the security and workload isolation properties of traditional VMs with the speed, agility and resource efficiency enabled by containers. They provide a secure, trusted environment for multitenant services, while maintaining minimal overhead.