Lately, IT teams are looking for Docker monitoring tools as legacy products like cAdvisor and Sysdig cloud do not offer greater insights. Docker monitoring needs a crucial change in the way your agents, plug-ins, and monitoring works to support containers. Many enterprises have started deploying significant production workloads on Docker, it seems there is a substantial shift towards adopting Docker.

Since a book series called “… for Dummies” was launched, people have finally come to realize how much they don’t know about the particular area they thought they previously knew about. We’ve all started from somewhere. Self-learning, learning at school or even in the office at our jobs. A breakthrough was achieved when we realized that something absolutely strange to us, became quite familiar.

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.

As we’ve discussed in part one and part two of this series, Service Level Objectives (SLOs) are essential performance indicators for organizations that want a real understanding of how their systems are performing. However, these indicators are driven by vast amounts of raw data and information. That being said, how do we make sense of it all and quantify our SLOs? Let’s take a look.

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.

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.

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.