One of the most common reasons for system failures is changes to the underlying infrastructure. Amazon CloudTrail does a great job of recording when actions are taken but a lot of organizations don’t take advantage of it. FireHydrant now includes this data, giving you visibility into changes to your infrastructure while you’re investigating an incident.

A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.

A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.

Many clients have asked me, “When do I use DynamoDB streams vs Kinesis?” It’s a great question, since both services are very similar, especially when you process their records with Lambda functions. So let’s break it down and look at the key differences between Kinesis and DynamoDB streams. When it comes to streaming and processing real-time events on AWS, Kinesis is the de facto solution in AWS.

Last week I was battling jet lag and this week, an inbox of unanswered emails. But I’m also marveling at the quality of the AWS community all over the world. I had the privilege of speaking at ServerlessDays Sydney the week before last, and along with the amazing conference talks, I also got to see firsthand just how much of the globe is involved in pushing forward serverless using AWS technology and how excited they were to share what they were learning.

With the emergence of Amazon Web Services (AWS) in 2002, the Infrastructure as a Service (IaaS) model kickstarted the public cloud revolution that has permanently changed how IT services are deployed and delivered. According to Gartner’s July 2019 Magic Quadrant for Cloud Infrastructure as a Service report, the annual cloud IaaS market is currently worth $41.4 billion and is expected to grow to $81.5 billion by 2022.

Since it was released in 2015, the Serverless Framework has become the community-favorite way to manage and deploy serverless applications in the cloud. Similar to Terraform and CloudFormation, it lets you express infrastructure as code, making it easy to share and version-control your entire serverless environment.

Hybrid monitoring has increasingly become a priority in the SCOM community. A growing number of enterprises are running workloads both on-prem and in the cloud, with some of us creating new workloads in the cloud, and others engaging in a gradual migration to the cloud – a process that often takes place over a number of years. How can we deliver a complete monitoring strategy for our hybrid environments via SCOM?