This project is a helper for creating scripts that run in both AWS Glue, Jupyter notebooks, and in docker containers with spark-submit. Glue supports running Zepplin notebooks against a dev endpoint, but for quick dev sometimes you just want to run locally against a subset of data and don't want to have to pay to keep the dev endpoints running.

Glue has specific methods to load and save data to s3 which won't work when running in a jupyter notebook. The glueshim provides a higher level api to work in both scenarios.

from aws_glue_etl_docker import glueshim
shim = glueshim.GlueShim()

params = shim.arguments({'data_bucket': "examples"})
pprint(params)


files = shim.get_all_files_with_prefix(params['data_bucket'], "data/")
print(files)

data = shim.load_data(files, 'example_data')
data.printSchema()
data.show()

shim.write_parquet(data, params['data_bucket'], "parquet", None, 'parquetdata' )
shim.write_parquet(data, params['data_bucket'], "parquetpartition", "car", 'partitioneddata' )

shim.write_csv(data, params['data_bucket'],"csv", 'csvdata')

shim.finish()

Running locally is easiest in a docker container

1. Copy data locally, and map that folder to your docker container to the /data// path.
2. Start docker container, map your local notebook directory to /home/jovyan/work

Example Docker command docker run -p 8888:8888 -v "$PWD/examples":/home/jovyan/work -v "$PWD":/data jupyter/pyspark-notebook

import sys
!{sys.executable} -m pip install git+https://github.com/purecloudlabs/aws_glue_etl_docker

For deployment to AWS, this library must be packaged and put into S3. You can use the helper script deploytos3.sh to package and copy.

Usage ```./deploytos3.sh s3://example-bucket/myprefix/aws-glue-etl-jupyter.zip

Then when starting the glue job, use your S3 zip path in the Python library path configuration

The shim is currently setup to delete any data in the output folder so that if you run with bookmarks enabled and then need to reprocess the entire dataset and

aws_glue_etl_docker can also be used as a cli tool to clean up Jupyter metadata from a workbook or convert it to a python script.

The clean command will open all workbooks in a given path and remove any metadata, output and execution information. This keeps the workbooks cleaner in source control

aws_glue_etl_docker clean --path /dir/to/workbooks

The build command will open all workbooks in a given path and convert them to python scripts. Build will convert any markdown cells to multiline comments. This command will not convert any cells that contain #LOCALDEV or lines that start with ! as in !{sys.executable} -m pip install git+https://github.com/purecloudlabs/aws_glue_etl_docker

aws_glue_etl_docker build --path /dir/to/workbooks