Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.

Serverless has the potential to bring massive ops advantages to projects of all sizes, but while it presents great business benefits, we need to spare a thought for how teams develop on serverless. I recently published ‘Serverless Development is Broken’ a list of concerns about how developers can work with long deploy times inherent in a cloud-only code environment.

We’re pleased to announce that the Lumigo serverless intelligence platform now supports the Python microframework, AWS Chalice. Chalice was created by AWS to simplify the process of writing serverless apps in Python. Similar in structure to the Flask web framework, Chalice handles much of the configuration on behalf of the user, including automated IAM role policy generation.

Many people writing about AWS Lambda view Node as the code-default. I’ve been guilty of this in my own articles, but it’s important to remember that Python is a ‘first-class citizen’ within AWS and is a great option for writing readable Lambda code. Take a look at these two starter examples of writing functionality in Python.

An ever increasing number of developers and architects are using AWS Lambda to deploy serverless functions to control costs and reduce the burden of server management. Due to the unpredictable nature of end-user demand in today’s digital-first world, Lambda functions can help resolve unplanned scaling issues.

Serverless architectures such as AWS Lambda have created new challenges in debugging code. Without a solid logging framework in place, you could waste hours, or even days, tracking down simple defects in your functions. A strategic logging framework can be a powerful way to track down and resolve bugs. Let’s walk through how to get the most out of logging Lambda functions.

This is the first of a two-part blog series. In this post we’ll use Stackery to configure and deploy a serverless data processing architecture that utilizes AWS Step Functions to coordinate multiple steps within a workflow. In the next post we’ll expand this architecture with additional workflow logic to highlight techniques for increasing resiliency and reliability.

The way we build software products changes all the time. Serverless architecture opens up new opportunities to create awesome products faster. Not only that, serverless applications are more scalable, secure and maintainable – all with much less code. Serverless is the future of development and you should get ready for it. But what does it mean to be a serverless developer? What do you need to understand before jumping into the serverless pool?

We’re very pleased to announce the official launch of our new Java tracer for AWS Lambda. This release extends Lumigo’s real-time monitoring and observability tools to all JVM-based AWS Lambda applications, with support for Apache Groovy, Scala and Kotlin among others.