with the glue job aws-glue-export-job.py, create a Glue job in your AWS environment with :
- Glue version set to Glue v3
- A IAM role capable to write to one of your S3 bucket
edit this job script to set TARGET_BUCKET to your target S3 bucket, on which will be stored the AWS Glue v3 backup
- From the S3 bucket, download the glue3.zip file
- convert this zip file to tgz archive, and rename it 'glue3-opt-amazon.tgz', making sure the archive preserver the /opt/amazon folder structure
- copy the tgz file into the docker/zeppelin or docker/jupyter folder of this cloned repo
cd docker/zeppelin
docker build -t gluedev .
docker run -it --rm --name gluedev -p 8080:8080 -p 7077:7077 -p 9001:9001 -v $PWD/logs:/logs -v $PWD/notebook:/notebook -e ZEPPELIN_LOG_DIR='/logs' -e ZEPPELIN_NOTEBOOK_DIR='/notebook' -v /path/to/your/.aws:/root/.aws:ro gluedev
The .aws volume is only required if you want your local job to get access to your AWS environment (ie. bucket S3).
http://localhost:9001
cd docker/jupyter
docker build -t gluedev .
docker run -it --name gluedev -p 4041:4040 -p 8889:8888 -e JUPYTER_ENABLE_LAB=yes -v ~/.aws:/home/jovyan/.aws:ro -v ~/YOUR_LOCAL_JUPYTER_FOLDER:/home/jovyan gluedev
-
The .aws volume is only required if you want your local job to get access to your AWS environment (ie. bucket S3).
-
The folder
~/YOUR_LOCAL_JUPYTER_FOLDERis used for sharing the notebook files with your host machine.
Check output of the previous command to get the token. For instance:
http://127.0.0.1:8888/?token=2f79af840450b332bea0d34e16f1189acab90449a5caec62
To enable AWS S3 access with your AWS credential just add this line in your notebook :
spark.sparkContext._jsc.hadoopConfiguration().set("fs.s3a.aws.credentials.provider", "com.amazonaws.auth.DefaultAWSCredentialsProviderChain")
(source: http://wrschneider.github.io/2019/02/02/spark-credentials-file.html)