Cloud applications have been the talk of the town for several years now. Especially when it comes to cost reduction and more efficient use of available resources, the cloud is hard to beat. Its true potential only becomes apparent when cloud-optimized architectures and design patterns are used. This enables stable software to be developed and complex requirements to be broken down into small, manageable solutions. But this advantage comes at a price. Questions start to arise like: "How can services communicate with each other when systems fail?" and "How do I deal with peak loads?"
It’s been a few weeks since we wrapped up another great edition of the Serverless Architecture Conference but we still remember the amazing talks! One of them was Gareth McCumskey's keynote, “Why local development for serverless is an anti-pattern”, where he argues that in the serverless community, there is no need to waste a lot of time and effort building an environment that is a replica of the cloud.
If you haven't shouted "Bingo" yet, you have only yourself to blame. How can it be possible to use almost all of the bleeding-edge technologies, frameworks and platforms listed above successfully together in a real-world project away from the greenfield and Hello World demos? A field report.
Today’s data landscape is overflowing with complex and sophisticated architectures, which can help you dynamically customize your digital ecosystem according to your project requirements and needs. Unfortunately, managing complex cloud architectures can be a difficult task, especially if you are trained to use one cloud vendor and not any of the others. Database as a Service (DBaaS) offerings can help you fill this gap. DBaaS models provide cloud users with managed database offerings. This article reviews DBaaS models offered by the top three cloud vendors—AWS, Azure, and Google Cloud.
In the future, many companies will try to grow their IT infrastructure with the help of the cloud or even move it completely to the cloud. Larger enterprises often call for multicloud. In terms of serverless, there are a few ways to achieve multicloud operation. Using AWS Lambda, a function can be made available and the whole thing can be made cloud-independent with Knative.
Serverless architectures are the next step in the evolution of cloud services. The first attempts at walking with them are easy. However, you should know the stumbling blocks in order to be able to avoid them. This article presents the typical challenges with corresponding possible solutions.
Java is still the first choice when it comes to software development for business use . However, the development of Java software alone is not enough: machines, operating systems, JREs, application servers, etc. are required for productive use - and large frameworks and libraries are also required as the basis for code functionality. This overhead hurts more the simpler the required functionality is, because it makes development, testing, and operation more difficult. The alternative concept: Serverless.
Why does it have to be "Serverless or Microservices"? It should be "Microservices with Serverless"! Based on some of the generally accepted principles of microservices, we can use serverless architectures and technologies to build highly focused microservices. Let's take a pragmatic and concise approach to building microservices with Azure Functions, Azure Service Bus, Azure Storage, and other services and tools. And it works for almost all software developers: Java, .NET, Node.js, and even Python.
In times when ever larger amounts of data are processed and the speed of development in IT is - in order to keep up with the competition - becoming increasingly sought after, it is more and more important to be able to react scalably to a growing business volume and to establish a fast development and innovation cycle. This is exactly where serverless can help, as it eliminates much of the complexity of operation and allows you to bring speed to development.