Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to the final installment of our Complete AWS Lambda Handbook series! Given Lambda is often the central point for many serverless applications, we wanted to make sure we didn’t skip or breeze past any part. In this episode, we’re looking at some limitations and difficulties using AWS Lambda and how to overcome them, and the importance of monitoring for performance and failure remediation.

So you’ve got an app ready for launch, and unlike in the past where you simply ran it on-premises, this time you want to try the cloud. You know AWS is the leading cloud platform, and decide to give it a go. The first thing you’ll bump into as you learn about AWS is the various options of cloud instances available with AWS EC2.

With the economies of many countries heading towards recession, increasing regulations, growing security threats and increasing costs, financial services firms need practical solutions, now more than ever. Many of the financial technology firms (‘fintechs’) and mobile app-based challenger banks have taken advantage of modern application architectures and DevOps practices that are associated with cloud native technologies.

Amazon EKS is the most popular managed Kubernetes solution. DevOps teams can quickly spin up clusters in the cloud and get started with Kubernetes in a few clicks. As organizations embrace Kubernetes in the cloud, the challenge becomes managing clusters across multiple regions or accounts. At that point, organizations struggle to visualize all of their clusters.

AWS Elasticsearch is a common provider of managed ELK clusters., but does the AWS Elasticsearch pricing really scale? It offers a halfway solution for building it yourself and SaaS. For this, you would expect to see lower costs than a full-blown SaaS solution, however, the story is more complex than that.

Google Operations suite, formerly Stackdriver, is a central repository that receives logs, metrics, and application traces from Google Cloud resources. These resources can include compute engine, app engine, dataflow, dataproc, as well as their SaaS offerings, such as BigQuery. By shipping this data to Elastic, you’ll get a unified view of the performance of resources across your entire infrastructure from cloud to on-prem.

"A long time ago in a data centre far, far away.... Our first episode takes us back to the early days of launching Civo, where we had a fledgling Ceph cluster with a bunch of SSDs in each. Those were darker times, in the early days of OpenStack. As time went by the cluster grew, new bigger nodes were added along with bigger disks.

In collaboration with the AWS team, we have just launched another AWS integration, the X-ray data source. Combined with the CloudWatch and Timestream integrations, the AWS X-Ray data source simplifies monitoring and triaging with one Grafana console. The addition of the AWS X-ray data source reflects Grafana’s commitment to becoming a full observability platform that supports distributed tracing as well as metrics and logs.

This latest release of SquaredUp for SCOM and SquaredUp for Azure is focused on addressing the most popular enhancements that our customers have requested.