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Personal take on GraphDB + AML with AWS Neptune + Glue + Lambda.
Python 54.85%
Shell 0.71%
Jupyter Notebook 44.44%
aws-neptune-aml's Introduction
- This project explores the use case of anti-money laundering / fraud detection using Graph Databases
- Data set has been obtained from Kaggle: https://www.kaggle.com/ntnu-testimon/paysim1
- Aims to create an end to end solution from cloud architecture to application / api
- app: Application / API source code. Runtime python2.7 on AWS Lambda + AWS API Gateway
- data: data source, data collection
- etl: ETL code running on AWS Glue
- infra: Infrastructure as Code to spin up all resources in AWS using Cloudformation
Event Driven ETL Architecture
![Event Driven ETL Architecture AWS Neptune Diagram](/neptune-event-driven.png?raw=true)
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