Operations | Monitoring | ITSM | DevOps | Cloud

It’s been a great couple of months at Stackery. Since coming on as CEO earlier this year, I’ve been impressed with how much our team gets done and their contributions to making the serverless development experience easier and more reliable. I want to take a moment to recognize two of our amazing Stackerinos who were recognized by AWS recently with the AWS Serverless Hero distinction.

Managing a cloud system properly entails numerous tasks (performance monitoring, response times, latency, uptime management, security, compliance, and disaster recovery). Together, these tasks form a comprehensive holistic strategy that embraces and accounts for all potential scenarios. Especially in today’s intensive business landscape, running cloud operations (NOC) 24/7 has become mandatory together with maintaining a high availability of network services.

In part 2 of our AWS S3 Monitoring series, we covered the basics of AWS S3 logging, why it’s important to log all the information in your cloud environment, and also the benefits of monitoring those logs. Now, AWS offers some great tools for monitoring and log querying, but if you and your team want to take it to the next level, Sumo Logic is there for your needs.

Nowadays, it’s not uncommon to see enterprise IT leaders in a situation that seems like a catch 22. Oftentimes, they are expected to be involved in making data-driven decisions for augmenting productivity and profitability. Paradoxically, they are preoccupied with what they consider as their core responsibilities – applying best practices to safeguard the IT infrastructure and expediting investigations when incidents occur.

Azure Monitor gathers performance metrics from your various Azure resources and allows you to explore those metrics through visualizations. It also allows you to manually create alerts that will notify you when a metric crosses a predefined threshold. In this blog post, we’ll cover how to create an alert in Azure Monitor.

If you have anything to do with the world of cloud computing or even programming for that matter, then I’m sure you’ve heard of different terms being tossed around such as “serverless computing” or “containers,” and even “monolithic architectures.” A lot of people who understand such computing methods can have a bad habit of using these terms without leaving any explanation as to what they are.

First time this year, multi-cloud enterprises, as a customer segment of Sumo Logic, have grown faster than any other segment: 50% Y/Y. What took so long? In my conversations with enterprises over the last 5 years, there was only one strategy for public cloud and it was multi-cloud. But evidence of multi-cloud usage was sparse at best. Data from our Continuous Intelligence Report in previous years didn’t find much to support that the strategy for multi-cloud was being implemented.

In Part 1 of this series, we saw how Amazon MQ routes messages between services in a distributed application, and we looked at some of the key metrics that describe the performance of the message broker and its destinations. Now that we’ve introduced the metrics and their meaning, we’ll look at some tools you can use to collect and query metrics from Amazon MQ:

In Part 2 of this series, we showed you how to use CloudWatch to monitor metrics and logs from Amazon MQ. With CloudWatch, you can easily create ad-hoc graphs to visualize the performance of your messaging infrastructure and other AWS services you use (such as EC2, Lambda, and S3). But to monitor your Amazon MQ brokers, destinations, and clients alongside the rest of your applications and infrastructure, you need a monitoring platform that easily integrates with your whole technology stack.