Do you run software that provides locally available health checks via a webserver only reachable via localhost? In this blog post, I will show you an architecture that you can use to connect those local health checks to CloudWatch Logs and even receive alarms if things are not going to plan.
Every machine has recurring tasks. Backups, updates, runs of configuration management software like Chef, small scripts, … But one of the problems in a cloud environment is visibility. Instead of scheduling dozens of cron jobs or tasks per instance, would it not be nice to have a central service for this? You already have. And it’s called EventBridge…
When setting up a microservice architecture, each individual service is often owned and managed by a different team. To achieve a higher level of resource isolation, and allow for more granular security and cost management, each service team usually deploys its resources into a dedicated AWS account. While this type of distributed approach offers many benefits in terms of productivity, scalability, and resiliency, it introduces another layer of complexity in regard to AWS cross-account communication and microservice consumption. In this blog post, I would like to show you how you can leverage AWS services like Amazon API Gateway, Lambda, DynamoDB, and VPC Endpoints in combination with Terraform to build a fully-managed and serverless cross-account microservice architecture.
When setting up IPSec VPN connections between different companies, the connecting parties often require the tunnel to use public IP addresses as the encryption domain. Especially when establishing a connection to telecommunication partners, the usage of public addresses is often mandatory and ensures that there are no overlapping addresses across other connections. In this blog post, I would like to show you how you can leverage tools like pfSense and VNS3 in combination with Terraform to build a Site-to-Site IPSec VPN connection between AWS and on-premises networks with a public encryption domain.
Sometimes, developing a fully-fledged Lambda is not feasible, is too much work, or is simply outside of the knowledge domain of the people involved. But what if we could run plain Bash scripts instead?
When deciding which Infrastructure as Code tool to use for deploying resources in AWS, Terraform is often a favored choice and should therefore be a staple in every DevOps Engineer’s toolbox. While Terraform can increase your team’s performance quite significantly even when used locally, embedding your Terraform workflow in a CI/CD pipeline can boost your organization’s efficiency and deployment reliability even more. By adding automated validation tests, linting as well as security and compliance checks you additionally ensure that your infrastructure adheres to your company’s standards and guidelines. In this blog post, I would like to show you how you can leverage the AWS Code Services CodeCommit, CodeBuild, and CodePipeline in combination with Terraform to build a fully-managed CI/CD pipeline for Terraform.
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.
