Operations | Monitoring | ITSM | DevOps | Cloud

AWS Lambda has gained good traction for building applications on AWS. But, is it really the best fit for all use cases? Since its introduction in 2014, Lambda has seen enthusiastic adoption – by startups and enterprises alike. There is no doubt that it marks a significant evolution in cloud computing, leveraging the possibilities of the cloud to offer distinct advantages over a more traditional model such as EC2.

Cloud spend optimization is a challenging endeavor for most companies. The major reason for this is that most don’t have sufficient visibility into their live cloud costs, so they can’t tie day-to-day decision-making to those staggering AWS bills that arrive at the end of each month (way too late to make meaningful changes).

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.

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.

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.

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?