Welcome to the final installment of our Complete AWS Lambda Handbook series! Given Lambda is often the central point for many serverless applications, we wanted to make sure we didn’t skip or breeze past any part. In this episode, we’re looking at some limitations and difficulties using AWS Lambda and how to overcome them, and the importance of monitoring for performance and failure remediation.

AWS DynamoDB changed the database game in Serverless and continues to do so, as its design repeatedly proves its huge value. This guide takes you through everything there is to know about DynamoDB so you can rest assured you’re using the service in its best way and reaping all of the benefits.

Now that you’ve chosen to go the serverless route, which vendor option should you go for? That’s one of the major questions that anybody asks themselves when they make the switch. Should you choose Amazon Web Services (AWS), which is a mature service that will serve you well, or should you go with e Google’s younger Firebase? Before the comparison of AWS vs Firebase, we should understand what serverless really means. This one server can be responsible for several different functions.

In Part 1 of our Complete AWS Lambda Handbook for Beginners, we gave a refresher on the fundamentals of AWS Lambda and what is AWS Lambda. In this post, we’ll look at AWS Lambda pricing, some interesting Lambda facts and examples of great AWS Lambda use cases in your serverless application.

To get some serious work done, we usually need to prepare for it. “Baby steps first,” they say. In our niche, these “baby steps” would be countless small jobs that need to be done before we can start with our main project. Proper preparation(try saying this 5 times fast) is the key to success, but after we’ve achieved our primary goal, there will always be something to do to keep it steady and flowing.

Welcome to the Serverless world. One of the first things you’ll hear about is AWS Lambda - and you’ll continue to keep hearing about it! While architecture can be serverless without Lambdas involved, it’s very often the key component within a serverless application. In the first post of this 3-part AWS Lambda Handbook series, we run through what is AWS Lambda, dialling back to basics with the various terminology, how to create a Lambda function and how to run it.

We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

In this article I’ll be sharing my own lessons learned with Dashbird in building reliable Well-Architected serverless applications and the highest impact areas to focus on no matter where you are in the Serverless journey.

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring? Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible and measuring resource consumption for each invocation is possible.