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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 aws-neptune aws-glue etl graphdb graph-database aml finance anti-money-laundering aws-lambda serverless python zappa

aws-neptune-aml's Introduction

Overview

  • 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

Project Structure

  • 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

AWS Neptune Diagram

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