Building a serverless application means you usually trade in old issues for new ones. This is an attempt to create a decision framework and break down arguments for and against using serverless vs. other computing models.

Handling large images has always been a pain in my side since I started writing code. Lately, it has started to have a huge impact on page speed and SEO ranking. If your website has poorly optimized images it won’t score well on Google Lighthouse. If it doesn’t score well, it won’t be on the first page of Google. That sucks.

More groups are progressing from just talking about Event-Driven Automation to actually doing it in practice. StackStorm helps make this easy. When organizations start offloading business-critical tasks and automating for real it becomes essential to ensure that the Automation engine itself is not a single point of failure when it is responsible for recovering a fleet of servers, managing datacenters, and automating remediations.

Cloud-native development and microservices enable development teams to work more efficiently and innovate faster. Operators appreciate the container environment because it increases infrastructure utilization, enabling them to accomplish more with less while managing critical applications at unprecedented scale. Kubernetes and containers adoption growing fast in the last few years.

While working for Dashbird.io I’ve had to pleasure to come in contact with a number of serverless early adopters that included both small companies working on apps or just testing ideas as well as fortune 500 companies with an already established user base. What I’ve found is that a lot the people I speak to think of serverless as a shortcut to developing software but that’s just not the case.

This week at the Microsoft Ignite, we unveiled two new Sumo Logic applications for Microsoft Azure services — Azure SQL Database and Azure Active Directory — and two new native integrations with Azure Monitor and Blob Storage. As a cloud-native company, our goal at Sumo Logic is to give our customers the flexibility to create digital IT and DevOps initiatives that leverage multi-cloud deployments in Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure.

In an ideal world, your DevOps toolchain would be highly automated for incident management and allow your teams to resolve issues at DevOps speed. An alert triggered by monitoring tools like Datadog or AWS Cloudwatch would notify on-call engineers, kick your collaboration tools into gear (ChatOps, StatusPage, etc), and automatically document the issue in ITSM and ticketing tools.

On this episode of Exception Perceptions, GitHub Community Engineer Bex Warner stopped by to chat about Probot, a framework for automating GitHub. Watch the episode, and then read what else Bex has to say about the magic of Probot. Also, check out this example of what Sentry has built with Probot.