The purpose of this repository is to demonstrate the impact design decisions have while working with Amazon DynamoDB tables.
It is very common for developers that come from a relational background (RDBMS) to try to replicate the same normalization concepts in Amazon DynamoDB tables, trying to obtain the scalability, cost efficiency and throughput that Amazon DynamoDB can provide, however, data normalization could lead to data models that are not efficient specially from the cost perspective.
All the data, code and access patterns from this application was generated for sample purposes, even if we have seen customers implement very similar strategies Customer should always load test their scenarios and confirm it satisfies their application requirements, in terms of cost, response time, scalability and security.
Before any production release, customers should conduct a risk assessment and incorporate additional security controls as required based on data classification and use cases. It is customer responsibility to ensure their application code satisfies their own internal security postures and implement mechanisms to mitigate and prevent sensitive data exposure.
You can download NoSQL Workbench for Amazon DynamoDB which is a cross-platform client-side GUI application for modern database development and operations and is available for Windows, macOS, and Linux. NoSQL Workbench is a unified visual IDE tool that provides data modelling, data visualization, and query development features to help you design, create, query, and manage DynamoDB tables.
Visit the AWS documentation for more information about NoSQL workbench for Amazon DynamoDB.
This repository contains NoSQL datamodels to help you understand the difference between a normalized and a denormalized data model, please visit the /workbench folder for sample data models and sample csv information.
All the fake information in this repository that you can find in the /data folder, was generated using Mockaroo but you can use any tool that works for your situation, with the free samples on Mockaroo you can generate very complex datasets that are very useful specially in data denormalization scenarios.
The files in the /app folder are intended to help you visualize the difference in performance and cost while working with denormalized data models. For more information please visit the INFO section.
Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional documentation, we greatly value feedback and contributions from our community. Please revisit the CONTRIBUTING file.
This library is licensed under the MIT-0 License. See the LICENSE file.