A lot of people think that using the cloud makes you elastic, but cloud really only gives you the potential to be elastic. If your apps aren’t elastic, and your cloud usage doesn’t flex up and down based on activity levels, then it’s really just expensive. The same is true of containers—they don’t magically make all of your hosting problems go away, and while they have the potential to make you more agile and efficient, it isn’t that simple.

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring? Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible, and measuring resource consumption for each invocation is possible.

2020 has seen a surge in the number of organizations embracing remote work to adapt to the new normal, leading to increased reliance on digital collaborative workspaces like Microsoft Teams. As organizations migrate critical business processes to such digital workspaces, IT service management teams need to leverage these platforms as channels for delivering IT and enterprise support.

While Spot by Netapp allows organizations to reliably and affordably run production environments on spot instances, operation teams always want to be alerted about any events of interest that occur in their environment. Over the last 5 years, Spot by NetApp customers have been using our existing notification mechanism and have shared excellent feedback for various enhancements and new capabilities.

Another generic error message from our favorite FaaS provider AWS Lambda. And again, there are multiple reasons why this issue could arise. Let’s first look at the basics of AWS Lambda to get a better intuition for when things go wrong later. Lambda is an asynchronous event-based service at heart.

We’ve been monitoring 100,000’s of serverless backend components for 3+ years at Dashbird. In our experience, Serverless infrastructure failures boil down to: These isolated faults become causes of failure due to dependencies in our cloud architectures (ref. Difference of Fault vs. Failure). If a serverless Lambda function relies on a database that is under stress, the entire API may start returning 5XX errors.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.