Can it handle extremely high write volumes?

Yes, it includes specific logic for workloads exceeding 10,000 writes/sec, recommending data binning, write sharding, and batch processing strategies to optimize throughput.

How does this skill help with partition key selection?

The skill analyzes your specific access patterns and data distribution to suggest the most efficient Partition Key or Hierarchical Partition Key, preventing performance bottlenecks and hot partitions.

Does this skill provide cost estimations?

It provides detailed Request Unit (RU) calculations based on your document sizes and access frequency, helping you predict and manage your Azure consumption costs.

What documentation does the skill produce?

It generates and maintains two primary files: 'cosmosdb_requirements.md' to track discovery details and 'cosmosdb_data_model.md' for the final technical design and justifications.

Azure Cosmos DB Data Modeling

Name: Azure Cosmos DB Data Modeling
Author: atc-net

byatc-net

•

データベース管理

Architects optimized Azure Cosmos DB NoSQL data models by analyzing access patterns, partition strategies, and throughput requirements.

This skill provides a structured framework for designing high-performance Azure Cosmos DB NoSQL databases. It guides developers through an interactive discovery process to document application requirements, access patterns, and volumetrics before generating a comprehensive data model. By applying advanced NoSQL patterns—such as aggregate-oriented design, hierarchical partition keys, and data binning—it ensures your database remains cost-effective and scalable, particularly for massive-scale workloads exceeding 10,000 writes per second.

主な機能

01Optimization patterns for massive-scale ingestion and data binning

021 GitHub stars

03Interactive requirement gathering for access patterns and workload volumetrics

04Advanced partitioning strategy selection including Hierarchical Partition Keys

05Accurate RU cost estimation based on realistic document sizes and overhead

06Automated generation of requirements and final data model documentation

ユースケース

01Refactoring an existing Cosmos DB container to resolve hot partitions or high RU costs

02Designing a new NoSQL schema for a greenfield Azure cloud application

03Modeling high-throughput IoT or telemetry systems requiring massive-scale write strategies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add atc-net/atc-agentic-toolkit azure-cosmosdb-datamodeling

For use in Claude.ai and ChatGPT

主な機能

01Optimization patterns for massive-scale ingestion and data binning

021 GitHub stars

03Interactive requirement gathering for access patterns and workload volumetrics

04Advanced partitioning strategy selection including Hierarchical Partition Keys

05Accurate RU cost estimation based on realistic document sizes and overhead

06Automated generation of requirements and final data model documentation

ユースケース

01Refactoring an existing Cosmos DB container to resolve hot partitions or high RU costs

02Designing a new NoSQL schema for a greenfield Azure cloud application

03Modeling high-throughput IoT or telemetry systems requiring massive-scale write strategies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add atc-net/atc-agentic-toolkit azure-cosmosdb-datamodeling

For use in Claude.ai and ChatGPT