One of the great promises of serverless has always been that it would free developers to focus on writing code without having to give too much consideration to the underlying infrastructure. But the advantages presented by the instantly, infinitely scalable nature of serverless come with limitations and unique considerations that you need to take into account.
Most enterprises don't use "the cloud," but rather an intricate mix of hybrid, multi, public and private clouds. Their success depends on their ability to orchestrate and automate this complex environment into a single, smooth workflow.
The typical AWS bill, otherwise known as the AWS Cost and Usage Report, includes line items that are useful to both finance and DevOps. However, many of the metrics that are within engineers’ and cloud architects’ control aren’t so simple to discover. To make cost a first-class operational metric for DevOps, teams need visibility into the data that’s relevant to engineering activity.
A while back, I wrote about how you can shave latency off every AWS SDK operation by enabling HTTP keep-alive, like this. It had the desired effect and I saw lots of people apply this technique in their projects. But it also resulted in the same 10 lines of code being copied and pasted everywhere! I began thinking about ways to distribute an optimized version of AWS SDK so everyone can benefit.
If you are hosting your application with Heroku, and find yourself faced with an unexplained error in your live system. What would you do next? Perhaps you don’t have a dedicated DevOps team, so where would you start your investigation? With Scout APM of course! We are going to show you how you can use Scout to find out exactly where the problem lies within your application code.
Monitoring an Azure environment can be a challenging task for even the most experienced and skilled team. Applications deployed on Azure are built on top of an architecture that is distributed and extremely dynamic. But all is not doom and gloom. Azure users have a variety of tools they can use to overcome the different challenges involved in monitoring their stack, helping them gain insight into the different components of their apps and troubleshoot issues when they occur.
AWS CloudWatch Logs gives you full visibility into your AWS infrastructure, from individual workloads to the services that bind them. Monitoring these logs helps ensure their smooth and continued operation, ongoing stability, and performance. Integrating CloudWatch Logs with LogDNA makes it easier to parse, search, and analyze AWS logs in order to detect anomalies and troubleshoot problems faster.
Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.
It’s five o’clock on a Friday afternoon. There are no new bug reports and everything is looking smooth. Your plan of a relaxing weekend is in sight when you get a call—the website you look after isn’t responding. Yikes. AWS Lambda minimizes the chance of this truly terrifying event from happening by taking care of server maintenance while you focus on coding robust applications.
Serverless first came onto the scene in 2014 when AWS Lambda was launched. It offers a dynamic cloud-computing execution model where the server is run by the cloud provider. As with any relatively recent technology, its novelty results in a steep learning curve, and it comes with its own set of benefits and drawbacks.
