https://github.com/kwame-mintah/aws-lambda-data-preprocessing
A lambda function to perform data preprocessing on new data placed into an AWS S3 Bucket.
https://github.com/kwame-mintah/aws-lambda-data-preprocessing
aws aws-lambda data-preprocessing python python312
Last synced: 9 months ago
JSON representation
A lambda function to perform data preprocessing on new data placed into an AWS S3 Bucket.
- Host: GitHub
- URL: https://github.com/kwame-mintah/aws-lambda-data-preprocessing
- Owner: kwame-mintah
- Created: 2023-12-01T22:05:43.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-24T18:42:35.000Z (over 1 year ago)
- Last Synced: 2025-02-01T11:43:47.555Z (11 months ago)
- Topics: aws, aws-lambda, data-preprocessing, python, python312
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# AWS Lambda Data Preprocessing
[](https://www.python.org/downloads/release/python-3121/)
[](https://github.com/kwame-mintah/aws-lambda-data-preprocessing/actions/workflows/bump-repository-version.yml)
[](https://github.com/kwame-mintah/aws-lambda-data-preprocessing/actions/workflows/push-docker-image-to-aws-ecr.yml)
[](https://github.com/kwame-mintah/aws-lambda-data-preprocessing/actions/workflows/run-linter.yml)
A lambda to perform data pre-processing on new data put into an S3 bucket. An assumption has been made that new data
uploaded will be of the same format e.g. same features, data schema etc. Actions performed not limited to removing missing
data, imputing numerical values and/or categorical values etc.
This repository does not create the S3 Bucket, this is created via Terraform found here [terraform-aws-machine-learning-pipeline](https://github.com/kwame-mintah/terraform-aws-machine-learning-pipeline).
Data uploaded into these buckets can be found here [ml-data-copy-to-aws-s3](https://github.com/kwame-mintah/ml-data-copy-to-aws-s3). Additionally, data preparation is
specific to a specific set of data found within the GitHub repository.
# Flowchart
The [diagram below](https://mermaid.js.org/syntax/flowchart.html#flowcharts-basic-syntax) demonstrates what happens when the lambda is trigger, when a new `.csv` object has been uploaded to the S3 Bucket.
```mermaid
graph LR
S0(Start)
T1(Pull dataset from S3 Bucket)
T2(Dataset transformed using Pandas)
T3(Upload transformed data to output bucket)
T4(Tag original dataset as processed)
E0(End)
S0-->T1
T1-->T2
T2-->T3
T3-->T4
T4-->E0
```
# Notice
As mentioned in the project description, the code provided here is to be used for a specific dataset which is predicting
if potential customers will engage with offers provided to them. Using various information, such as demographic, past
interactions, and environmental factors and only target a specific set of customers with an offer.
Data pre-processing if done incorrectly is one of the biggest risk in every machine learning project. Please ensure that
enough time is taken and the data received is properly understood before attempting to automate the process.
## Development
### Dependencies
- [Python](https://www.python.org/downloads/release/python-3120/)
- [Docker for Desktop](https://www.docker.com/products/docker-desktop/)
- [Amazon Web Services](https://aws.amazon.com/?nc2=h_lg)
## Usage
1. Build the docker image locally:
```shell
docker build --no-cache -t data-preprocessing:local .
```
2. Run the docker image built:
```shell
docker run --platform linux/amd64 -p 9000:8080 data-preprocessing:local
```
3. Send an event to the lambda via curl:
```shell
curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}'
```
Example AWS S3 event received
```json
{
"Records": [
{
"eventVersion": "2.1",
"eventSource": "aws:s3",
"awsRegion": "eu-west-2",
"eventTime": "2023-12-01T21:48:58.339Z",
"eventName": "ObjectCreated:Put",
"userIdentity": { "principalId": "AWS:ABCDEFGHIJKLMNOPKQRST" },
"requestParameters": { "sourceIPAddress": "127.0.0.1" },
"responseElements": {
"x-amz-request-id": "BY65CG6WZD6HBVX2",
"x-amz-id-2": "c2La85nMEE2WBGPHBXDc5a8fd28kEpGt/QsP8n/xmbLv0ZAJeqsK/XmNcCCS+phWuVz8KP3/gn3Ql3/z7RPyC3n176rqpzvZ"
},
"s3": {
"s3SchemaVersion": "1.0",
"configurationId": "huh",
"bucket": {
"name": "example-bucket-name",
"ownerIdentity": { "principalId": "ABCDEFGHIJKLMN" },
"arn": "arn:aws:s3:::example-bucket-name"
},
"object": {
"key": "data/bank-additional-full.csv",
"size": 515246,
"eTag": "0e29c0d99c654bbe83c42097c97743ed",
"sequencer": "00656A54CA3D69362D"
}
}
}
]
}
```
## GitHub Action (CI/CD)
The GitHub Action "🚀 Push Docker image to AWS ECR" will check out the repository and push a docker image to the chosen AWS ECR using
[configure-aws-credentials](https://github.com/aws-actions/configure-aws-credentials/tree/v4.0.1/) action. The following repository secrets need to be set:
| Secret | Description |
|--------------------|------------------------------|
| AWS_REGION | The AWS Region. |
| AWS_ACCOUNT_ID | The AWS account ID. |
| AWS_ECR_REPOSITORY | The AWS ECR repository name. |