Yesterday our founder and CEO Nate Taggart announced his decision to step down from his post. While his presence will be missed by all of us, he made this choice in order for Stackery to bring in a new CEO with the experience and pedigree to steer the business to the next level.

When I browse Stackery’s documentation and blog, I see some great writing that I know not everyone has read. Check out these great articles essential to growth as a developer.

API Gateway is a powerful tool for creating a coherent API out of a set of multiple disconnected remote function providers, and API Gateway with AWS Lambda, when used properly, can give you the capacity to create truly powerful serverless APIs. However, given that API Gateway is a layer between your requests and the code itself, there are latency concerns that API Gateway Lambdas may face that may not be present when working with other architectures.

In this post, we’ll learn what Amazon Web Services (AWS) Lambda is, and why it might be a good idea to use for your next project. For a more in-depth introduction to serverless and Lambda, read AWS Lambda: Your Quick Start Guide to Going Serverless. In order to show how useful Lambda can be, we’ll walk through creating a simple Lambda function using the Python programming language. We’ll test it out, as well as take a look at what Lambda provides for metrics and logging.

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.

Ever wondered what’s under the hood of your neighbors’ car, the situation in their wallet or the configuration of their serverless stack? Well wonder no more! Today we will bring you the statistics of Dashbird so you could compare your Lambda functions with others. Unfortunately, the car and the wallet thingy you should figure out on your own. Let’s start… (I hope you like charts)

I was recently called upon to secure an Nginx web server with HTTPS, and my goal was to set this up with a certificate obtained from AWS Certificate Manager. It took me a while to figure out how to get everything configured and working. Hopefully someone else who is attempting to do the same thing will read this and I can save you some time!

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.

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.

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.