Recent Articles on the tecRacer AWS Blog

I need to start this blog post with a confession. But only if you promise to not tell anything to my tecRacer colleagues, deal? So, I built a monolith. And I loved it. And you will likely love it too if you decide to try out Laravel. Luckily, there is a service called Vapor that will let you deploy your Laravel project on serverless AWS infrastructure. That means I can continue to enjoy the top-notch developer experience of Laravel while also getting all the benefits of serverless technologies (plus, I can maintain a healthy relationship with my colleagues 🎉). But let’s start at the beginning.

When leveraging AWS services such as EC2, ECS, or EKS, achieving standardized and automated image creation and configuration is essential for securely managing workloads at scale. The concept of a Golden AMI is often used in this context. Golden AMIs represent pre-configured, hardened and thoroughly tested machine images that encompass a fully configured operating system, essential software packages, and customizations tailored for specific workload. It is also strongly recommended to conduct comprehensive security scans during the image creation process to mitigate the risk of vulnerabilities. By adopting Golden AMIs, you can ensure consitent configuration across different environments, leading to decreased setup and deployment times, fewer configuration errors, and a diminished risk of security breaches. In this blog post, I would like to demonstrate how you can leverage AWS CodePipeline and AWS Stepfunctions, along with Terraform and Packer, to establish a fully automated pipeline for creating Golden AMIs.

Presentation Deploying resources with infrastructure as code is the recommended way to provision resources in AWS. The native AWS-way of doing it is by using Cloudformation or CDK (Cloud Development Kit), and you should of course do this from day one. But in real world sometimes somebody provisioned resources via the console, or there is a need of refactor your code and split your stack into multiple stacks. Luckily It is not very often we have cases where it’s required to import resources.

Bedrock is now available in eu-central-1. It’s time to get real and use it in applications. Reading all blog posts about Bedrock, you might get the impression that Python and LangChain is the only way to do it. Quite the opposite! As Bedrock makes calling the models available as AWS API, all AWS SDKs are supported! This post shows how to use Bedrock with Python, Javascript and GO.

Galeria Karstadt Kaufhof and tecRacer have been collaborating on developing Galeria’s Cloud Platform for many years with tecRacer as their Go-To Partner for everything related to AWS. The work we have been doing ranges from designing, setting up and operating their landing zone and network infrastructure, managing platform security and cost optimizations all the way to deploying complex system architectures such as Kubernetes. Most notably, we have been one of their trusted partners when it comes to their SAP infrastructure on AWS and have supported in many successful SAP system deployments.

One of the most unknown options to access a VPC is Client VPN. Nearly all customers I am talking to are using a Bastion Host or similar to access services within their VPC. But what about direct access without any jumps in between? After reading this blog, you can create your own Client VPN.

RAG is a way to approach the “hallucination” problem with LLM: A contextual reference increases the accuracy of the answers. Do you want to use RAG (Retrieval Augmented Generation) in production? The Python langchain library may be too slow for your production services. So what about serverless RAG in fast GO Lambda?

In this blog post, we’ll explore how you can teach the DynamoDB Table resource in boto3 (and the client) to store and retrieve Python’s datetime and float objects, which they can’t do natively. We’ll also discuss why you should or shouldn’t do that.