Event-driven applications have become the foundation for developing modern digital applications. Application workflows are easier to automate with serverless frameworks, and Amazon EventBridge has revolutionized the way serverless applications are built. Since serverless is the new cool kid in the town, there has been a lot of infrastructure reengineering. This blog describes how alert-driven business logic can automate serverless workflows using Amazon EventBridge.

When thinking about serverless applications, one thing that comes to mind immediately is efficiency. Running code that gets the job done as swiftly and efficiently as possible means you spend less money, which means good coding practices suddenly directly impact your bottom line. How does logging play into this, though? Every logging action your application takes is within the scope of that same performance evaluation.

One of the benefits of Serverless architectures is the possibility of scaling applications without worrying about load balancers and clusters of servers. While services like AWS Lambda hold their promises on this area, there are usually misconceptions about how they work. It is common for developers to assume that Lambda functions can scale infinitely, at any speed, in any circumstances. Reality is quite different from that.

Serverless computing has taken off in recent years as engineering organizations have shied away from the complexity and cost that comes with managing physical servers and even virtual machines hosted on a cloud like AWS. If you are interested in serverless options on AWS, you should be comparing Lambda and ECS Fargate to figure out what fits your use case best. In this blog post we will compare these two AWS services in the following areas...

Today, the AWS Lambda platform has added a new arrow to its quiver – the ability to integrate with Amazon Elastic File System (EFS) natively. Until now, a Lambda function was limited to 512MB of /tmp directory storage. While this is sufficient for most use cases, it’s often prohibitive for use cases such as Machine Learning, as Tensorflow models are often GBs in size and cannot fit into the limited /tmp storage.

Serverless computing has taken off in recent years with more efficient cloud services. AWS Lambda is a great example of this, where provisioning and management of resources happens from the service’s end. You only have to deal with the code. This article will give a brief overview of AWS Lambda in contrast to EC2 instances, then walk through shipping AWS Lambda metrics to Logz.io.

Lambda is an AWS service that allows you to run your code completely in the cloud without the need for on-prem servers. As the world has shifted away from on-prem and toward serverless computing, Lambda has grown in popularity: 1700 companies report using Lambda as part of their data infrastructure in StackShare.