Which databases does this skill support?

While it specializes in MongoDB and DynamoDB, the principles it applies are relevant for most document and key-value NoSQL databases.

How does it handle performance optimization?

The skill focuses on denormalization, primary/secondary index selection, and sharding strategies to ensure the database can handle high read/write throughput.

Does it provide specific code for database initialization?

It generates schema definitions and table structures that can be used in initialization scripts or Infrastructure-as-Code templates.

Can this skill help migrate from SQL to NoSQL?

Yes, it can analyze existing relational schemas and suggest optimized NoSQL equivalents based on your application's access patterns.

NoSQL Data Modeler

Name: NoSQL Data Modeler
Author: BbgnsurfTech

byBbgnsurfTech

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Database Management

Architects efficient NoSQL database schemas and document structures for high-performance applications like MongoDB and DynamoDB.

About

The NoSQL Data Modeler skill empowers Claude to design production-ready NoSQL data structures by analyzing specific application requirements and query access patterns. It provides expert guidance on critical architectural decisions such as choosing between embedding and referencing, selecting optimal sharding keys, and implementing strategic denormalization. This skill is essential for developers looking to translate complex data requirements into scalable, low-latency NoSQL implementations while adhering to industry best practices for document and key-value stores.

Key Features

3 GitHub stars
Access pattern and query optimization
Denormalization and embedding strategies
Schema creation for MongoDB and DynamoDB
Sharding and partition key selection
Relational to NoSQL model translation

Use Cases

Creating high-throughput DynamoDB tables for social media activity
Designing an e-commerce product catalog with embedded metadata
Migrating legacy SQL structures to document-based NoSQL models

About

Key Features

3 GitHub stars
Access pattern and query optimization
Denormalization and embedding strategies
Schema creation for MongoDB and DynamoDB
Sharding and partition key selection
Relational to NoSQL model translation

Use Cases

Creating high-throughput DynamoDB tables for social media activity
Designing an e-commerce product catalog with embedded metadata
Migrating legacy SQL structures to document-based NoSQL models