projection-patterns

Projections transform event streams into queryable read models by applying event data to update materialized views or search indexes. This Skill provides Python templates for various projection types to maintain denormalized data for fast querying in event-sourced systems.

You can populate Elasticsearch from an event sourcing system by implementing projection templates that consume event streams and update search indexes. This Skill provides specific Elasticsearch integration examples for building and maintaining search indexes from events.

You create materialized views for real-time dashboards by using projection templates to process event streams into queryable formats. This Skill provides live and persistent projection patterns in Python to continuously update dashboard data from event streams.

Available projection types include live, catchup, persistent, and inline projections in Python. These templates support creating read models for materialized views, real-time dashboards, and search indexes within event-driven and event-sourced architectures.

Yes, you need an existing event-sourced architecture because projection patterns process event streams to build read models. This Skill focuses on creating CQRS read sides and materialized views from existing event data rather than generating the event streams themselves.