Serverless architecture has emerged as a pivotal technology for developers seeking to build scalable, efficient applications without the overhead of managing servers. At the heart of this revolution are services like AWS Lambda and API Gateway, which have redefined how we deploy and scale applications, offering a truly pay-as-you-go model that aligns operational costs directly with actual usage.

Serverless architectures have revolutionized how businesses deploy applications, offering scalability and cost efficiency by abstracting infrastructure management. This model, charging only for resources consumed, poses unique budgeting and cost optimization challenges due to its variable pricing structure. Navigating cloud costs in a serverless environment requires a nuanced understanding of leveraging this technology’s benefit while effectively managing expenses.

As software systems become more complex, navigating their inner workings has become increasingly difficult due to the evolution of more advanced architectures. While distributed systems, such as microservices and cloud-native architectures, offer benefits in scalability and agility, they also make it more challenging to pinpoint and resolve system issues. Traditional methods for tracking errors are often insufficient in these multifaceted environments.

Standardized frameworks play a fundamental role in leveling the playing field and setting the standard within the tech industry, ensuring that everyone has access to the same tools and practices. These frameworks promote best practices and foster innovation and collaboration across different sectors. One example of such a framework is OpenTelemetry, a project that has rapidly gained traction and continued to flourish as an open-source initiative under the Cloud Native Computing Foundation (CNCF).

In Part 1 of this blog, we prepared our demo container environments using Docker for the Node Express and Python Flask applications. Now, we move on to the more complex phase of our exploration, where we will dissect and explain the inner workings of our applications. This sequel is designed for those who want to improve their web development skills, offering a comprehensive guide to debugging and tracing.

This is the first post in a series of two looking at debugging and tracing MySQL, which has been a foundation stone of the tech industry, utilized by applications big and small, from personal blogs to complex e-commerce platforms. MySQL has demonstrated adaptability and robustness countless times, making it a critical part of the Internet’s infrastructure. This adaptability has helped MySQL remain relevant amidst the constantly evolving technological landscapes.

In this post, I describe how to send OpenTelemetry (OTel) data from an AWS Lambda instance to Honeycomb. I will be showing these steps using a Lambda written in Python and created and deployed using AWS Serverless Application Model (AWS SAM).

Having delivered numerous applications, prototypes, and demos over the years, I’ve developed a deep appreciation for how robust development frameworks can significantly contribute to Speed to Delivery Time (SDT). This metric is vital in the fast-paced software industry, where the ability to bring scalable and maintainable applications to market quickly can set a project apart.

As devs, we know the age-old question of, “How long do you think it will take?” oh too well. Regardless of experience and position within the development cycle, this inquiry always comes loaded with expectations and complexities, a concept I refer to as Speed to Delivery Time (SDT). To be clear, SDT isn’t just about marking days on a calendar.