Operations | Monitoring | ITSM | DevOps | Cloud

Serverless first came onto the scene in 2014 when AWS Lambda was launched. It offers a dynamic cloud-computing execution model where the server is run by the cloud provider. As with any relatively recent technology, its novelty results in a steep learning curve, and it comes with its own set of benefits and drawbacks.

It’s five o’clock on a Friday afternoon. There are no new bug reports and everything is looking smooth. Your plan of a relaxing weekend is in sight when you get a call—the website you look after isn’t responding. Yikes. AWS Lambda minimizes the chance of this truly terrifying event from happening by taking care of server maintenance while you focus on coding robust applications.

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.

Recent headlines surrounding big-name IPOs, such as that of Slack and Lyft, have highlighted the very real costs of operating in the cloud. Companies like these are on the hook to pay AWS and other public cloud vendors tens or hundreds of millions of dollars every year, just to run their services.

While deploying updates in large-scale production environments, it can be easy to overlook minor issues that may later turn out to be the cause of major infrastructure problems. To ensure these large deployments are safely rolled out to production, they can be staged in one subset of your environment and then another; for example, once an update is deployed to a subset, it is monitored to make sure everything is fine and then moved to the next subset.

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.

“To have your head in the clouds” has several meanings since Cloud computing arrived and is part of our vocabulary thanks to its efficiency. Therefore, today we will review not only what cloud computing is, but also the types of cloud computing.We also love to have our heads “in the clouds”.

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.