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Serverless

The latest News and Information on Serverless Monitoring, Management, Development and related cloud technologies.

Tracking IDocs for Integration Scenarios with Serverless360 BAM

Suppose you are a user of the Microsoft integration stack, and your organization also uses SAP. In that case, you will likely have use cases where an IDOC triggers integration processes in SAP being published. One of the good things about Logic Apps on Azure is that a connector for SAP allows you to register to receive IDOCS published by SAP, and you can then use them in your integration processes.

Observing Schrödinger's Python App

As a developer, I love the versatility of Python. Over the years I have used Python for so many different use cases: game development, APIs, IoT, machine learning, and web development. It can scale tall applications in a single bound and take on any challenge faster than you can pip install flask. Something you learn very quickly in the world of app development is to build everything for scale.

Serverless observability: Lumigo or AWS X-Ray

Observability is a measure of how well we are able to infer the internal state of our application from its external outputs. It’s an important measure because it indirectly tells us how well we’d be able to troubleshoot problems that will inevitably arise in production. It’s been one of the hottest buzzwords in the cloud space for the last 5 years and the marketplace is swamped with observability vendors. Different tools employ different methodologies for collecting data.

Deploy a serverless workload on Kubernetes using Knative and ArgoCD

Containers and microservices have revolutionized the way applications are deployed on the cloud. Since its launch in 2014, Kubernetes has become a standard tool for container orchestration. It provides a set of primitives to run resilient, distributed applications. One of the key difficulties that developers face is being able to focus more on the details of the code than the infrastructure for it. The serverless approach to computing can be an effective way to solve this problem.

Fargate Vs. Lambda: The Last Comparison You'll Ever Need

Things are changing. Technology differences between serverless and container-based systems are rapidly blurring. In 2020, Amazon Web Services (AWS) enabled AWS Lambda to package and deploy functions as container images, instead of bundling all of a function’s code and dependencies in a.zip file. Today, more organizations are increasingly deploying Lambda functions as Docker container images. These companies want to reap the benefits of serverless computing, containers, and container orchestration.

Using Lumigo OpenTelemetry Distributions with other backends

When we set out to trace applications running outside of AWS Lambda, there was little doubt in our minds that building on top OpenTelemetry was by far the best course of action. There are many reasons for this, but chiefly, it is a question of coverage. At its most fundamental level, achieving coverage requires as-wide-as-possible support for technologies, and interoperability among instrumentations.

Azure VM health monitoring - what's right for you?

Virtual Machines (VMs) are virtual computers with dedicated amounts of RAM, CPU power, and storage borrowed from a physical host computer. A Virtual Machine is a computer file, typically an image, that acts like a real computer. A Virtual Machine can have any operating system that runs in a window as a separate computing environment. Users can choose between the Linux distribution or Windows Server in the operating system.

Why and How to Monitor Amazon OpenSearch Service

Some time ago, AWS forked ElasticSearch, the most popular search engine on the planet. They had some struggles with the maintainer of ElasticSearch and decided it was time to part ways. So, with OpenSearch, there is now a new kid in town. Well, not new, but at least some kind of alternative.

Defining and measuring your SLIs and SLOs

Customers expect that online services are available all the time. The truth is that outages happen to almost everyone because providing 100% service availability is challenging and costly. Creating reliable and profitable service is, amongst other things, finding the balance between application availability, costs and time to market. Faster feature delivery means less availability as constant changes to production may cause issues and introduce bugs.