How does it handle write conflicts?

The skill implements optimistic concurrency control using per-stream versioning, ensuring that events are only appended if the expected version matches the current state.

Does it support real-time data updates?

Yes, it includes patterns for event subscriptions and global positioning, which are essential for building real-time projections and reactive systems.

Which databases does this skill support?

It includes implementation guides and schema templates for EventStoreDB, PostgreSQL, DynamoDB, and Kafka.

What is the main purpose of this skill?

It provides specialized patterns and templates for building the storage layer of event-sourced applications, ensuring data integrity and scalability.

Event Store Design & Implementation

Name: Event Store Design & Implementation
Author: 3commas-io

by3commas-io

Database Management

Design and implement high-performance event stores with robust patterns for event-sourced architectures.

About

This skill provides a comprehensive framework for architecting and deploying event stores, the backbone of event-sourced systems. It offers domain-specific guidance on choosing the right storage technology—whether it's EventStoreDB, PostgreSQL, or DynamoDB—and includes production-ready templates for schemas, optimistic concurrency control, and event subscriptions. By following these patterns, developers can ensure their event logs are immutable, ordered, and scalable, facilitating complex state management, audit trails, and real-time data processing across microservices.

Key Features

Technology comparison matrix for EventStoreDB, Kafka, PostgreSQL, and DynamoDB
Architecture blueprints for aggregate streams and global position tracking
Production-ready SQL and Python templates for event persistence and retrieval
0 GitHub stars
Subscription and checkpoint management for real-time event-driven workflows
Optimistic concurrency control patterns using versioned streams

Use Cases

Building custom event storage infrastructure on top of existing SQL or NoSQL databases
Setting up real-time event subscriptions and projection engine foundations
Implementing optimistic concurrency and idempotent writes in event-driven systems

About

Key Features

Technology comparison matrix for EventStoreDB, Kafka, PostgreSQL, and DynamoDB
Architecture blueprints for aggregate streams and global position tracking
Production-ready SQL and Python templates for event persistence and retrieval
0 GitHub stars
Subscription and checkpoint management for real-time event-driven workflows
Optimistic concurrency control patterns using versioned streams

Use Cases

Building custom event storage infrastructure on top of existing SQL or NoSQL databases
Setting up real-time event subscriptions and projection engine foundations
Implementing optimistic concurrency and idempotent writes in event-driven systems