AWS recently introduced a new Lambda Telemetry API giving users the ability to collect logs, metrics, and traces for analysis in AWS services like Cloudwatch or a third-party observability platform like Coralogix. It allows for a simplified and holistic collection of observability data by providing Lambda extensions access to additional events and information related to the Lambda platform.

Amazon CloudWatch monitors metrics of your Amazon Web Services (AWS) resources in real time and can trigger alarms when a metric goes above or below certain thresholds. Typically, Amazon CloudWatch sends out alarms by posting a message to an SNS (Amazon Simple Notification Service) topic, which distributes the message via several mediums, including email, SMS, and Lambda functions. Setting a CloudWatch alarm can be complex.

The Serverless framework is an open-source framework written in Node.js that simplifies the development and deployment of AWS Lambda functions. It frees you from worrying about how to package and deploy the application to the cloud, so you can focus on your application logic. Serverless applications are distributed by design, so good code coverage is vital, and should include unit testing.

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.