Can I rebuild my read models using this skill?

Yes, it includes logic for Catchup projections which process historical events from the event store to rebuild or update read models from scratch.

Does this skill support multiple database types?

Yes, it includes implementation templates for relational databases like PostgreSQL (SQL) and document-based search engines like Elasticsearch.

How does it handle data consistency?

The skill provides patterns for Live, Catchup, Persistent, and Inline projections, allowing you to choose between strong or eventual consistency based on your specific application needs.

What are projection patterns used for?

Projection patterns are used to transform write-optimized event streams into read-optimized databases (read models), allowing for faster and more complex queries in event-sourced systems.

Projection Patterns for Event Sourcing

Name: Projection Patterns for Event Sourcing
Author: GaitanS

byGaitanS

Gestión de Bases de Datos

Builds efficient read models and materialized views from event streams to optimize query performance in CQRS and event-sourced architectures.

Acerca de

This skill provides a comprehensive toolkit for implementing projection patterns, enabling developers to transform event streams into optimized read models. It offers standardized templates for various projection types—including live subscriptions, historical catchups, and persistent checkpoints—ensuring data consistency across diverse storage engines like PostgreSQL and Elasticsearch. By automating the boilerplate of projector logic and event handling, it allows for the rapid creation of real-time dashboards, search indexes, and complex aggregations in high-performance backend systems.

Características Principales

Integration patterns for SQL (PostgreSQL) and Search (Elasticsearch) databases
0 GitHub stars
Implementation of CQRS read models and materialized views
Checkpoint management for resilient, resumable event processing
Standardized templates for Catchup and Persistent projections
Aggregation logic for real-time reporting and analytical dashboards

Casos de Uso

Building optimized query models in Microservices architectures
Creating full-text search indexes from domain events
Generating real-time business metrics and daily sales reports

Acerca de

Características Principales

Integration patterns for SQL (PostgreSQL) and Search (Elasticsearch) databases
0 GitHub stars
Implementation of CQRS read models and materialized views
Checkpoint management for resilient, resumable event processing
Standardized templates for Catchup and Persistent projections
Aggregation logic for real-time reporting and analytical dashboards

Casos de Uso

Building optimized query models in Microservices architectures
Creating full-text search indexes from domain events
Generating real-time business metrics and daily sales reports