In an era where cloud computing is revolutionizing businesses, the rise of a new discipline called FinOps is commanding attention. Financial Operations, or FinOps, brings a financial lens to the operational aspects of cloud computing, enabling organizations to drive cost efficiency, business value, and growth. But what exactly is FinOps and how can businesses leverage it in the realm of cloud computing? Understanding FinOps FinOps is a cultural shift, a practice that combines systems, best practices, and organizational culture to bring financial accountability to the variable spend model of cloud computing.

Amazon OpenSearch Service is a fully-managed solution for interactive log analytics, real-time application monitoring, website search, observability, and more. With a vast customer base, Amazon OpenSearch Service manages hundreds of thousands of clusters and processes trillions of requests monthly. The service provides seamless access for users through SAML-based federation for OpenSearch Dashboards, enabling single sign-on (SSO) on Amazon OpenSearch Service domains using existing identity providers (IdPs) like OneLogin. SAML simplifies the login process by allowing users to access multiple platforms with a single username and password, providing a safer and more convenient online experience. This guide will walk you through integrating OneLogin with OpenSearch Dashboards for a seamless SSO experience, leveraging Amazon OpenSearch Service’s capabilities.

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 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 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.

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 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.

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.

Managing security groups and their rules in AWS infrastructure can become cumbersome and error-prone as your environment grows. In this blog post, I introduce a Terraform solution that simplifies this process by using a Markdown table to define security group rules.