Set up the source PostgreSQL database with an outbox table, then run Debezium and an S3 sink connector locally with Docker Compose.

Create a development environment for AWS Glue 3.0 and later by building a custom Docker image, because AWS publishes no image for those versions.

Schedule an AWS Lambda function more often than once a minute, using Amazon SQS to get past the one invocation per minute limit of EventBridge rules.

Build development environments for AWS Glue 1.0 and 2.0 with the published 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.

Develop and test AWS applications against LocalStack, a mocking framework that runs AWS services on a local machine, demonstrated with a web service.

Host the web application that calls an R machine learning model on Amazon S3, so both the front end and the Lambda backend run without a server.

Expose an R machine learning model packaged in AWS Lambda through Amazon API Gateway, giving the model a callable HTTP endpoint on AWS.

In the previous post, it is discuss how to develop and package an R machine learning model. In this post, I'll illustrate how to deploy the model via AWS Lambda.

In this post, I'll demonstrate how to test and develop a logistic regression model developed in R. Also the model will be packaged for AWS Lambda.