Recent Articles on the tecRacer AWS Blog

When discussing high performant real-time event streaming, Apache Kafka is a tool that immediately comes to mind. Optimized for ingesting and transforming real-time streaming data in a reliable and scalable manner, a great number of companies today rely on Apache Kafka to power their mission-critical applications and data analytics pipelines. In this blog series, I would like to show you how you can leverage Amazon MSK and Terraform to set up a fully managed, cross-account Apache Kafka streaming pipeline on AWS. In this first part, we will set up the MSK Kafka cluster and producers. The second part will show you how you can set up distributed Kafka clients in different AWS accounts and communicate with the MSK cluster via AWS VPC Endpoints.

When discussing high performant real-time event streaming, Apache Kafka is a tool that immediately comes to mind. Optimized for ingesting and transforming real-time streaming data in a reliable and scalable manner, a great number of companies today rely on Apache Kafka to power their mission-critical applications and data analytics pipelines. In this blog series, I would like to show you how you can leverage Amazon MSK and Terraform to set up a fully managed, cross-account Apache Kafka streaming pipeline on AWS. In the first part, we already set up the MSK Kafka cluster and producers. The second part will show you how you can set up distributed Kafka clients in different AWS accounts and communicate with the MSK cluster via AWS VPC Endpoints.

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the first post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the second post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

When setting up a Jenkins build server on a physical machine, right-sizing can become a challenging task. Long idle times followed by high, irregular loads make it hard to predict the necessary hardware requirements. One solution to this problem is the deployment of a containerized Controller/Agent-based Jenkins setup and to offload workloads to dedicated, transient agents. This is the third post of a three-post series. In this series, I would like to show you how you can leverage AWS Fargate and Terraform to deploy a serverless as well as fault-tolerant, highly available, and scalable Jenkins Controller/Agent deployment pipeline.

High Performance Computing or HPC is coming up when thinking about analyzing models or while working at universities and other research and development organizations. In this article I will show you how to setup HPC, giving you some background information how it works in AWS and to run a small script in your own Cluster.

The world is changing, with new AI tools emerging every day. One such tool that has been making waves recently is ChatGPT. It is only the first of many such tools hitting the market, and it urges us to think about the future of our work. I recently used it to help with a standard task that I often perform and was amazed by how well it helped me to automate it.

People are constantly stumbling over the AWS Marketplace and their more than 30,000 public offerings. Restricting such a massive amount of products is mainly done with the necessary internal AWS Marketplace policy that you find in a document in document management systems like AWS WorkDocs. What would you say about better restricting the public AWS Marketplace with a private Marketplace?