We'll discuss how to implement data warehousing ETL using Iceberg for data storage/management and Spark for data processing. A Pyspark ETL app will be used for demonstration in an EMR local environment. Finally the ETL results will be queried by Athena for verification.

Recently AWS Glue 3.0 was released but a docker image for this version is not published. In this post, I’ll illustrate how to create a development environment for AWS Glue 3.0 (and later versions) by building a custom docker image.

In this post, I'll demonstrate how to build development environments for AWS Glue 1.0 and 2.0 using the Docker image and the Visual Studio Code Remote - Containers extension.

In this post, it is demonstrated how AWS Lambda can be integrated with Apache Airflow using a custom operator inspired by the ECS Operator.

Traefik is a modern HTTP reverse proxy and load balancer. In this post, it'll be demonstrated how path-based routing can be set up by Traefik with Docker. Also a centralized authentication will be illustrated with the Forward Authentication feature of Traefik.

In this post, I'll illustrate how a web service is created using FastAPI framework where tasks are sent to multiple workers. The workers are built with Celery and Rserve. Redis is used as a message broker/result backend for Celery and a key-value store for Rserve. Demos can be run in both Docker Compose and Kubernetes.

In this post, I'll demonstrate how to create a Linux development environment on Windows using WSL. Also an example app (Rserve web service with a sidecar container) on Minikube will be demonstrated.

LocalStack provides an easy-to-use test/mocking framework for developing AWS applications. In this post, I'll demonstrate how to utilize LocalStack for development using a web service.

In the previous posts, it is discussed how to package/deploy an R machine learning model with AWS Lambda and to expose the Lambda function via Amazon API Gateway. In this post, I'll demonstrate how to host a web application that services the backend API in a serverless environment.

In previous posts, we discussed how to package and deploy an R machine learning model via Lambda. In this post, I'll demonstrate how to expose the model via Amazon API Gateway.