Welcome once again to another Logic App Best practices, Tips, and Tricks. In my previous blog posts, I talked about some of the most essential best practices you should have while working with the Azure Logic App: And some tips and tricks: Today I’m going to speak about another critical Best practice, Tips, and Tricks that you need to implement while administrating your cloud integration resources: Control your connectors (API connections).

Losing track of communication between applications or code has become a problem with the tech world growing more into supporting Serverless cloud architectures and allowing the developer to maintain, upgrade and update these services. One might say that services and code are becoming more loosely coupled, allowing code to run and execute in silos. Let's take an AWS Lambda function as an example.

AWS Lambda is a compute service that lets you run code on high-availability infrastructure without any server provisioning. You can perform tasks such as maintenance of servers and operating systems, capacity provisioning, automatic scaling, and code logging and monitoring. When using AWS Lambda, you are just responsible for your code. Lambda manages the resources needed to run your code, like CPU, network infrastructure, and memory.

When a startup is in its very early stages, rapid iteration and dynamism are at the top of its priorities. The ability to do so, while maintaining a stable and high-quality product, is a big challenge facing the R&D group. We want to release features as quickly as possible, but this rapid velocity cane cause conflicts when writing in-depth, comprehensive tests.

Welcome once again to another Logic App Best practices, Tips, and Tricks. In my previous blog posts, I talked about some of the most essential best practices you should have while working with the Azure Logic App: And some tips and tricks: Today I’m going to speak about in my opinion one of the most annoying features/tasks and recommend some Best practices, Tips, and Tricks to minimize it: working with expressions.

Recently we had an interesting use case from our customer for which Serverless360 mapped a perfect solution. We resolved the issue with the new Log Analytics and Application Insights capabilities from Serverless360. This blog will help you better understand the feature set with a real-time use case.

Step Functions, the serverless finite state machine service from AWS. With DynamoDB, Lambda, and API Gateway, it forms the core of serverless AWS services. If you have tasks with multiple steps and you want to ensure they will get executed in the proper order, Step Functions is your service of choice. It offers direct integrations with many AWS services, so you don’t need to use Lambda Functions as glue. This can improve the performance of your state machine and lower its costs.

AWS announced the release of the Lambda Function URLs feature today. In this post, I describe what it is, how it works, and how you can benefit from it. API Gateway and AWS Lambda is a potent combination and lets you build REST APIs without having to worry about the underlying infrastructure. API Gateway offers many powerful features out-of-the-box, including: Understandably you pay a premium for these features.

Nowadays, microservice architecture is a pattern that helps to innovate quicker by enabling easier scalability, giving language flexibility, improving fault isolation, etc. Systems built this way also bring some downsides. Moving parts, concurrent invocations, and different retries policies can make operating and troubleshooting such systems challenging. Without proper tools, correlating logs with metrics may be difficult. To overcome these challenges, you need observability.

AWS Lambda is AWS’s solution for highly portable, serverless computing. With Lambda functions, you can deploy and run business logic code without managing the underlying servers. Today, AWS announced that Lambda customers can now provision up to 10 GB of ephemeral storage for each of their functions, making them well-suited for new, data-intensive workloads—including machine learning inference, large media file processing, financial analysis, and more.