Catalog Basics

A PanderaCatalog is the central in-memory registry for Pandera schemas.

Registering schemas

Use register() to add schemas. Each schema name must be unique unless overwrite=True is used.

catalog.register(
    "sales_data",
    schema,
    description="Schema for the daily sales feed",
    tags=["finance", "production"],
)

Looking up schemas

Use get() for the schema, or get_entry() for metadata too.

schema = catalog.get("sales_data")
entry = catalog.get_entry("sales_data")
print(entry.description)

Listing schemas

list() returns sorted schema names.

print(catalog.list())
# ['my_schema', 'sales_data']

Using a SQL backend

To persist entries and expose tabular SQL views, pass a SqlCatalogBackend into the catalog:

from pandera_catalog import PanderaCatalog, SqlCatalogBackend
from sqlalchemy import create_engine

engine = create_engine("postgresql+psycopg://user:pass@host/dbname")
backend = SqlCatalogBackend(engine=engine)
catalog = PanderaCatalog(backend=backend)

The backend creates normalized tables plus views including v_schema_catalog, v_schema_columns, v_schema_checks, v_projection_steps, and v_metadata_lookup. Projected schemas are materialized into schema/column/check views with is_projected and source_projection_name markers.

If you do not already have an engine, you can still pass a URL string and let the backend create one for you.