This is an Amazon EMR Studio project for CDK development with Python.
The cdk.json
file tells the CDK Toolkit how to execute your app.
This project is set up like a standard Python project. The initialization
process also creates a virtualenv within this project, stored under the .venv
directory. To create the virtualenv it assumes that there is a python3
(or python
for Windows) executable in your path with access to the venv
package. If for any reason the automatic creation of the virtualenv fails,
you can create the virtualenv manually.
To manually create a virtualenv on MacOS and Linux:
$ python3 -m venv .venv
After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.
$ source .venv/bin/activate
If you are a Windows platform, you would activate the virtualenv like this:
% .venv\Scripts\activate.bat
Once the virtualenv is activated, you can install the required dependencies.
(.venv) $ pip install -r requirements.txt
At this point you can now synthesize the CloudFormation template for this code.
(.venv) $ cdk synth --all \ -c vpc_name="your-vpc-name" \ -c emr_studio_name="your-emr-studio-name"
To add additional dependencies, for example other CDK libraries, just add
them to your setup.py
file and rerun the pip install -r requirements.txt
command.
Use cdk deploy command
to create the stack shown above.
(.venv) $ cdk deploy --require-approval never --all \ -c vpc_name="your-vpc-name" \ -c emr_studio_name="your-emr-studio-name"
For example,
(.venv) $ cdk deploy --require-approval never --all \ -c vpc_name="default" \ -c emr_studio_name="datalake-demo" EmrStudioStack: building assets... [0%] start: Building eb5eeb490dccbcd549ae27e0359b16b08361800c8444cf3e4a1c969a0c9c84e2:819320734790-us-east-1 [100%] success: Built eb5eeb490dccbcd549ae27e0359b16b08361800c8444cf3e4a1c969a0c9c84e2:819320734790-us-east-1 EmrStudioStack: assets built EmrStudioStack: deploying... [0%] start: Publishing eb5eeb490dccbcd549ae27e0359b16b08361800c8444cf3e4a1c969a0c9c84e2:819320734790-us-east-1 [100%] success: Published eb5eeb490dccbcd549ae27e0359b16b08361800c8444cf3e4a1c969a0c9c84e2:819320734790-us-east-1 ... Outputs: EmrStudioStack.EmrStudioDefaultS3Location = s3://datalake-demo-emr-studio-us-east-1-a4hzjvb EmrStudioStack.EmrStudioId = es-KWX8LX799XYDYTL7SAWH75UV EmrStudioStack.EmrStudioName = datalake-demo EmrStudioStack.EmrStudioUrl = https://es-KWX8LX799XYDYTL7SAWH75UV.emrstudio-prod.us-east-1.amazonaws.com
After an EMR Studio is successfully created, click EMR Studio Url (check out EmrStudioUrl
in CloudFormation Outputs section, e.g., https://es-KWX8LX799XYDYTL7SAWH75UV.emrstudio-prod.us-east-1.amazonaws.com).
When you use an EMR Studio, you can create and configure different Workspaces to organize and run notebooks.
Do the following steps to run your notebook.
- (Step 1) Create an EMR Studio Workspace.
- (Step 2) Launch a Workspace.
- (Step 3) Attach Jupyter Notebook to an EMR Cluster up and running.
Delete the CloudFormation stack by running the below command.
(.venv) $ cdk destroy --force --all
cdk ls
list all stacks in the appcdk synth
emits the synthesized CloudFormation templatecdk deploy
deploy this stack to your default AWS account/regioncdk diff
compare deployed stack with current statecdk docs
open CDK documentation
- (video) Modern Data Lake Storage Layers - Hudi vs. Iceberg vs. DeltaLake
- An Introduction to Modern Data Lake Storage Layers - Hudi vs. Iceberg vs. DeltaLake
Enjoy!