Operations | Monitoring | ITSM | DevOps | Cloud

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.

When it comes to cloud strategy, companies rank “cutting costs” as their top priority for 2019, according to a recent Datamation survey. That’s not to say that they plan to cut back on cloud spending in general; in fact, those budgets are very much expected to grow. Rather, companies are looking for ways to reduce unnecessary costs and optimize cloud spend.

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.

AWS has 21 (and counting!) geographic regions. If you never use one or more of those regions, detecting activity in those regions is critical for your cloud security posture.

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.