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CLEAN UP

NOTE: UNDER CONSTRUCTION - MAKE SURE

If you are at an AWS hosted event and used Event Engine for your labs, you can skip the following clean up steps.

If you are using your own AWS account for the labs, we strongly recommend you to perform the clean up steps as soon as you finish the labs to avoid extra charges.

Execute the commands below in Cloud9 terminal to delete the AWS resources created during labs.

If you choose to use names that are different than the default names provided in the lab instructions, please modify the names to match your own resources in the commands below.

1. Delete the DataBrew project, jobs, and dataset.

aws databrew delete-job --name <your jobs names>
aws databrew delete-job --name <your profile jobs names>
aws databrew delete-project --name <your projects names>
aws databrew delete-dataset --name <your datasets names>

2. Find the recipe versions in the recipe store and delete the versions list by below cli command.

aws databrew list-recipe-versions --name <you recipes names>

Replace the recipe version number with what you find from command above.

aws databrew batch-delete-recipe-version --name <your repices names> --recipe-version "1.0"

3. Delete all the Glue jobs, Crawlers and Databases created during the labs.

aws glue delete-job --job-name dummy-streaming-job
aws glue delete-job --job-name glue-streaming-job 
aws glue delete-job --job-name ml-notebook-job

aws glue delete-crawler --name <crawler names>

aws glue delete-database --name <database names>

5. Delete Triggers and Workflows

aws glue delete-workflow --name <your workflow name>
aws glue delete-trigger --name <your trigger name>

aws events list-targets-by-rule --rule <your rule name>
aws events remove-targets --rule <your rule name> --ids "<your target name 1>"
aws events remove-targets --rule <your rule name> --ids "<your target name 2>"

aws events delete-rule --name <your rule name>

aws stepfunctions delete-state-machine --state-machine-arn arn:aws:states:${AWS_REGION}:${AWS_ACCOUNT_ID}:stateMachine:<your state machine name>

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6. Delete the CloudTrail and the Workshop bucket.

aws s3 rm s3://${BUCKET_NAME} --recursive

7. Delete the lab Cloudformation stacks.

aws cloudformation delete-stack --stack-name etl-ttt-demo

8. Close the browser tab of Cloud9 IDE.

Now you have cleaned up all the resources created during the workshop.