database-explorer

To explore a PostgreSQL schema using SQLAlchemy async ORM, you introspect tables, columns, and relationships to build a navigable data map. This skill provides ready-made patterns to inspect catalog, orders, customers, and inventory contexts for the VorstersNV platform.

The best way to write async joins and aggregations for a PostgreSQL database is using SQLAlchemy's asynchronous ORM patterns. This skill demonstrates example queries with performance implications for filtering, joining, and aggregating across multiple data contexts.

Yes, you can use raw SQL patterns alongside SQLAlchemy ORM for querying PostgreSQL. This skill satisfies requirements for both ORM-style data access and raw SQL execution, providing examples for common tasks like filters and aggregations.

To handle lazy-loading considerations in async SQLAlchemy queries, you must account for performance implications when accessing related data. This skill explains how to navigate relationships across contexts like orders and customers while managing these loading behaviors.

Yes, this skill covers Alembic migration awareness for PostgreSQL databases. It includes SQL and Python snippets that demonstrate awareness of schema migrations alongside ORM-style data access and raw SQL patterns.