Serverless has become an increasingly popular paradigm among organizations looking to modernize their applications as it allows them to increase agility while reducing their operational overhead and costs. But the highly distributed nature of serverless architectures requires developers to rethink their approach to application design and development. AWS-based serverless applications hinge on AWS Lambda functions, which are stateless and ephemeral by design.

In a previous article, we talked about how you could use BAM to do advanced management of dead lettered messages on the Azure Service Bus using a scenario like below. This article will show how you can set up this kind of example. Let’s assume your Order Processing functions are already in place, and your starting point is that sometimes you will get dead-lettered messages, and we need to implement the Send message to the BAM logic app and configure Serverless360.

TL;DR: Dashbird launches observability for five new AWS services (ELB, SNS, RDS, OpenSearch, and HTTP API Gateway) to allow for a faster, more secure, and smoother serverless observability experience. Dashbird, the leading monitoring platform for serverless AWS applications, announces five new AWS integrations.

In this article, we will cover the basics of a Lambda function and its functionality in our every day digital lives. AWS Lambda, as we already know, is a compute service that allows you to run code without managing servers. AWS Lambda runs the code when it is needed, and it is automatically scaled. The code you execute on AWS Lambda is called Lambda function, and it can be considered, for better understanding, as a formula in a spreadsheet.

AWS Lambda is a serverless compute service that allows you to run code without having to manage servers. Lambda provides autoscaling and bills only on compute time, so you aren’t paying for unused resources. Some common use cases are file processing, stream processing, and acting as a backend for web and mobile applications. AWS Lambda functions can be invoked with external HTTP requests as well as by events triggered by over 200 different AWS services.

This blog is a write up on one of the best webinars hosted by Serverless360, “Building a frictionless Azure Support Workflow”, presented by Michael Stephenson. In this session, Michael Stephenson, Microsoft Azure MVP, who has extensive experience coaching organizations to adopt Microsoft Azure successfully, will walk us through the Serverless360, highlighting the product capabilities to address the business challenges from an operational and support perspective.

This blog is the continuation of the blog “Get the best out of Azure Data Factory – Part 1“. It explains how Serverless360 addresses the pain points and enhances the usage of Azure Data Factory. Serverless360 is a single platform solution that enables Operations and Support Teams to manage and monitor Azure Serverless services more efficiently. Let us have a quick recap of part 1 before looking into new features that Serverless360 provides for Azure Data Factory.

Did you get all the serverless updates from AWS pre- & re:Invent 2021?️ If you’re a serverless enthusiast we’ve collected the most important serverless updates from re:Invent 2021 you need to know about.

AWS Lambda is an incredible tool that works well with an abundance of other services on AWS. In this hands-on walkthrough, we’ll show you how to get started and create your first Node.js AWS Lambda function. Once upon a time, not so long ago, a word caught my ear. Lambda. That struck a chord, remembering the good old days of playing Half-Life as a kid. Little did I know what AWS Lambda was and how incredibly awesome it is. If you’re intrigued, stick around.

When you want to stay ahead and on top of things in a fast-moving industry, machine learning (ML) is surely one of the trending solutions. Today, innovative companies already have leading Machine Learning tools well-integrated into their processes. In comparison, your start could seem dreadfully slow. Or maybe you just don’t have the time or resources to invest in running your own Machine Learning training infrastructure.