In the fast-growing Azure space, it is essential to scale your business as Azure scales up. Azure is cost efficient by providing a pay-as-you-go model, but it is still necessary for enterprise users to undergo a Cost Analysis to keep their budget at stack. Let us consider a scenario, you’re a manager in your organization, and your team has been using Azure for the last several months. It has created multiple resources that cost money.

From our experience handling Azure Service Bus messages, one frequent suggestion we get from the support person is to download messages from Azure Service Bus entities like Queues and Topic Subscriptions. By downloading the messages as a local copy, it becomes easy for them to debug the messages and use it at a later point in time. Basic knowledge of Azure Service Bus messaging entities is a pre-requisite for the better understanding of this feature.

With serverless and containerized applications becoming a norm, workloads and integrations are spread across multiple cloud environments. As these apps become increasingly more distributed, monitoring also becomes more complicated with siloed and incomplete telemetry. This is where distributed tracing brings great value. It enables end-to-end visibility in your modern and complex application.

The internet of things is one of my favorite topics. IOT enables low-powered connected devices that opens gateways from the digital to the real world. While I love tinkering away with an Arduino sketch and the latest Espressif or Arduino board, there is always an air of frustration when trying to build out what at first seems like simple functionality using one of these “smart devices” because of the limited view we have into their operations.

Due to the increasing influence of businesses on APIs, the volume of APIs on which they rely, and the administrative challenges that APIs pose, API administration has gained popularity. For the most part, other apps don’t have the exact requirements or construction and management processes as APIs. Vital documentation, higher security standards, extensive testing, frequent versioning, and excellent reliability are all requirements for using APIs properly.

Lambda is the glue that holds serverless architectures together. Before its release, most users felt it was a matter of luck as to whether AWS would let you connect a service to another. If not, you had to spin up a VM or a container to transform the events from one service in a way that your target service could handle them. Since Lambda was easier to set up, people assumed that all code they would deploy on it would run faster and cheaper than on other compute services.

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.

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.

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.

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.