# Continuous Deployment for AWS Glue

DevFeed: [Continuous Deployment for AWS Glue](<https://devfeed.tech/articles/continuous-deployment-for-aws-glue-22993.md>)

Original publisher: [Read original article](<https://bravenewgeek.com/continuous-deployment-for-aws-glue/>)

Author: Mohammed

Published: 2020-10-15T15:51:25Z

Content type: tutorial

Language: en

Sources: [Brave New Geek](<https://devfeed.tech/sources/brave-new-geek.md>)

Topics: [AWS Glue](<https://devfeed.tech/topics/aws-glue.md>), [Continuous Deployment (CD)](<https://devfeed.tech/topics/continuous-deployment.md>), [GitHub Actions](<https://devfeed.tech/topics/github-actions.md>), [Jupyter Notebook](<https://devfeed.tech/topics/jupyter-notebook.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [analytics-pipeline](<https://devfeed.tech/tags/analytics-pipeline.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-glue](<https://devfeed.tech/tags/aws-glue.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [continuous-delivery](<https://devfeed.tech/tags/continuous-delivery.md>), [continuous-deployment](<https://devfeed.tech/tags/continuous-deployment.md>), [etl](<https://devfeed.tech/tags/etl.md>), [github](<https://devfeed.tech/tags/github.md>), [github-actions](<https://devfeed.tech/tags/github-actions.md>), [jupyter](<https://devfeed.tech/tags/jupyter.md>), [jupyter-notebook](<https://devfeed.tech/tags/jupyter-notebook.md>), [s3](<https://devfeed.tech/tags/s3.md>), [serverless](<https://devfeed.tech/tags/serverless.md>)

## AI overview

A tutorial for automating continuous deployment of AWS Glue ETL jobs. It uses GitHub Actions to generate a Python script from a Jupyter notebook, copy it to Amazon S3, and update the Glue job to use the new script.

## Source excerpt

AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It's a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job.