Operations | Monitoring | ITSM | DevOps | Cloud

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.

Being innovative is like being handsome. It only counts when others think so. When Bank of America honors you for being innovative and improving how their 30,000 developers perform, it’s a very handsome compliment. At the 11th Bank of America Technology Innovation Summit, JFrog was recognized for industrial leadership and excellence in providing global business solutions. This year, JFrog was one of only two technology companies honored for its strong partnership with the firm.

TL;DR: Fast-moving IT stacks see frequent, long and painful outages. Thousands of changes – planned, unplanned and shadow changes – are one of the main reasons behind this. Until now, IT Ops, NOC & DevOps teams didn’t have an easy way to get a real-time answer to the “What Changed?” question – the answer that can help reduce the duration of outages and incidents in these fast-moving IT stacks. Now, with BigPanda Root Cause Changes, they do.

Amazon MQ is a managed ActiveMQ messaging service hosted on the AWS cloud. Amazon MQ’s brokers route messages between the nodes in a distributed application. Each broker is a managed AWS instance, so your messaging infrastructure doesn’t require the maintenance and upfront costs of a self-hosted solution.

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.

To centralize logging from your entire stack—from traditional infrastructure to serverless components—Datadog is announcing native support for the launch of FireLens for Amazon ECS. FireLens streamlines logging by enabling you to configure a log collection and forwarding tool such as Fluent Bit directly in your Fargate tasks. We’ve partnered with AWS to provide built-in Fluent Bit support for Datadog so that you can now seamlessly route container logs from AWS Fargate.