ETL Pipeline

A Python ETL repository that ingests Kaggle datasets, runs local extract/transform/load workflows, and optionally uploads raw and processed data to AWS S3.

What this project contains

• Local Kaggle ingestion scripts for e-commerce, healthcare, finance, sports, and climate domains under ingest/
• A modular ETL pipeline under src/ and etl/
• Root environment configuration templates in .env.example and reusable settings under config/
• AWS helper code for S3 upload and optional Redshift load under src/cloud/
• E-commerce analytics SQL in analytics/ecommerce_queries.sql
• Warehouse schema DDL in warehouse/schemas/*.sql
• Monitoring examples in monitoring/
• A pytest-based test suite in tests/

Note: terraform/ contains a Terraform root configuration and AWS provider file, but the referenced Terraform module sources are not included in this repository. The supported workflow is local development with optional AWS helper support.

Status

• Local ETL and data ingestion are implemented in Python.
• AWS S3 upload and optional Redshift helper methods exist, but full multi-service cloud provisioning is not available in this checkout.
• dags/ and k8s/ provide deployment skeletons rather than a complete cloud production stack.
• .env is a local configuration file that should not be committed.
• Data directories under data/ are excluded from version control and should be created locally.
• This repository is best used for local pipeline development, testing, and Kaggle ingestion.

Quick Start

Prerequisites

• Python 3.10+
• Git
• pip
• Kaggle account + API credentials
• Optional: AWS CLI and AWS credentials for S3 upload

Local setup

git clone https://github.com/Victor-Kipruto-Rop/cloud-etl-pipeline.git
cd cloud-etl-pipeline
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
mkdir -p data/raw data/processed data/analytics

Run the local pipeline

python -m ingest.kaggle_ingest --domain ecommerce

The ingest/kaggle_ingest.py script downloads Kaggle dataset files into data/raw/ and can optionally trigger local processing workflows.

Run the AWS helper locally

AWSS3BUCKET=your-bucket \
AWS_REGION=us-west-1 \
KAGGLE_DATASET=olistbr/brazilian-ecommerce \
KAGGLE_DOWNLOAD=true \
KAGGLEFORCEDOWNLOAD=true \
.venv/bin/python3 -m src.cloud.aws_etl

This command downloads the specified Kaggle dataset, processes CSV files, writes Parquet outputs to data/processed/, uploads raw CSV files to S3, and optionally uploads processed Parquet files.

Run tests

.venv/bin/python3 -m pytest -q

Supported workflows

• ingest/: dataset download and ingestion orchestration
• src/pipeline.py: local ETL orchestration
• src/extract/, src/transform/, src/load/: pipeline stages
• src/cloud/aws_etl.py: AWS helper orchestration
• src/cloud/aws_s3.py: S3 upload utilities
• src/cloud/aws_redshift.py: Redshift load helper
• dags/: Airflow DAG skeleton for ecommerce ETL orchestration
• k8s/: Kubernetes ETL job manifest skeleton

Project structure

cloud-etl-pipeline/
├── README.md
├── ARCHITECTURE.md
├── DEPLOYMENT.md
├── IMPLEMENTATION_SUMMARY.md
├── RELEASE_NOTES.md
├── CREDIBILITYAUDITFIXES.md
├── TROUBLESHOOTING.md
├── PROJECT_STRUCTURE.md
├── requirements.txt
├── pyproject.toml
├── .env.example
├── config/
│   ├── aws_config.yaml
│   └── domains.yaml
├── .github/
│   └── workflows/
│       ├── ci.yml
│       ├── lint.yml
│       ├── python-app.yml
│       ├── run_pipeline.yml
│       ├── deploy_glue.yml
│       └── deploy_terraform.yml
├── ingest/
│   ├── kaggle_ingest.py
│   ├── ecommerce_ingest.py
│   ├── healthcare_ingest.py
│   ├── finance_ingest.py
│   ├── sports_ingest.py
│   ├── climate_ingest.py
│   └── config.py
├── src/
│   ├── api.py
│   ├── config.py
│   ├── dashboard.py
│   ├── health.py
│   ├── logging_config.py
│   ├── migrations.py
│   ├── pipeline.py
│   ├── validation.py
│   └── cloud/
│       ├── aws_etl.py
│       ├── aws_redshift.py
│       ├── aws_s3.py
│       └── init.py
├── etl/
│   ├── init.py
│   └── ecommerce_transform.py
├── analytics/
│   └── ecommerce_queries.sql
├── warehouse/
│   └── schemas/
│       ├── ecommerce.sql
│       ├── healthcare.sql
│       ├── finance.sql
│       ├── sports.sql
│       └── climate.sql
├── monitoring/
│   ├── alert_rules.yml
│   ├── docker-compose.monitoring.yml
│   ├── grafana-dashboard.json
│   ├── playbook.md
│   ├── prometheus.yml
│   └── README.md
├── diagrams/
│   └── system_diagrams.md
├── data/
│   ├── raw/
│   ├── processed/
│   └── analytics/
├── tests/
├── terraform/
│   ├── main.tf
│   ├── outputs.tf
│   └── variables.tf
└── infra/
    └── aws/
        └── provider.tf

Notes

• data/raw/ and data/processed/ are local working directories.
• terraform/ and infra/aws/ provide AWS configuration skeletons, but the repository is not a complete, runnable cloud deployment package on its own.
• Use the local pipeline path for development and testing.