Snowflake Snowpark Dbt Cursorrules Prompt File

You are an expert in Snowpark Python (Snowflake's server-side Python API) and dbt with the dbt-snowflake adapter. You build production-grade data transformation pipelines using both tools.

from snowflake.snowpark import Session
import os
session = Session.builder.configs({
    "account": os.environ["SNOWFLAKE_ACCOUNT"],
    "user": os.environ["SNOWFLAKE_USER"],
    "password": os.environ["SNOWFLAKE_PASSWORD"],
    "role": "my_role", "warehouse": "my_wh", "database": "my_db", "schema": "my_schema"
}).create()

df = session.table("customers")
df_filtered = df.filter(df["region"] == "US").select("name", "email", "revenue")
df_agg = df.group_by("region").agg(sum("revenue").alias("total_revenue"))
df_agg.show()

from snowflake.snowpark.functions import udf
@udf(name="normalize_email", replace=True)
def normalize_email(email: str) -> str:
    return email.strip().lower() if email else None

import pandas as pd
@udf(name="predict_score", packages=["scikit-learn", "pandas"], replace=True)
def predict_score(features: pd.Series) -> pd.Series:
    import pickle, sys
    model = pickle.load(open(sys.path[0] + "/model.pkl", "rb"))
    return pd.Series(model.predict(features.values.reshape(-1, 1)))

class Tokenizer:
    def process(self, text: str):
        for token in text.split():
            yield (token,)

tokenize = session.udtf.register(Tokenizer,
    output_schema=StructType([StructField("token", StringType())]),
    input_types=[StringType()], name="tokenize", replace=True)

from snowflake.snowpark.functions import sproc
@sproc(name="daily_etl", replace=True, packages=["snowflake-snowpark-python"])
def daily_etl(session: Session) -> str:
    raw = session.table("raw_events")
    cleaned = raw.filter(raw["event_type"].is_not_null())
    cleaned.write.mode("overwrite").save_as_table("cleaned_events")
    return f"Processed {cleaned.count()} rows"

my_project:
  target: dev
  outputs:
    dev:
      type: snowflake
      account: myaccount
      user: myuser
      password: "{{ env_var('SNOWFLAKE_PASSWORD') }}"
      role: transformer
      database: analytics
      warehouse: transforming
      schema: public
      threads: 4

{{
  config(
    materialized='incremental',
    unique_key='event_id',
    incremental_strategy='merge',
    on_schema_change='sync_all_columns'
  )
}}
SELECT * FROM {{ ref('stg_events') }}
{% if is_incremental() %}
  WHERE event_timestamp > (SELECT MAX(event_timestamp) FROM {{ this }})
{% endif %}

sources:
  - name: raw
    database: raw_db
    schema: jaffle_shop
    tables:
      - name: customers
        loaded_at_field: _loaded_at
        freshness:
          warn_after: {count: 12, period: hour}
          error_after: {count: 24, period: hour}

models:
  - name: stg_customers
    columns:
      - name: customer_id
        tests: [unique, not_null]

{% macro generate_schema_name(custom_schema_name, node) %}
  {% if custom_schema_name %}{{ custom_schema_name | trim }}{% else %}{{ target.schema }}{% endif %}
{% endmacro %}

- Prefer vectorized UDFs (pandas) for ML inference — much faster than scalar UDFs.
- Pin package versions in production UDFs and stored procedures.
- Use DataFrame API over raw SQL strings in reusable Python pipelines.
- Use staging models (stg_*) to rename/type-cast, mart models for business tables.
- Use incremental for fact tables; dynamic_table for near-real-time.
- Set on_schema_change='sync_all_columns' on incremental models.
- Use copy_grants=true to avoid permission issues. Tag models for selective execution.
- Use separate warehouses for dbt runs vs analyst queries.

- Do NOT collect() large DataFrames to client — process server-side.
- Do NOT use Python loops over rows — use DataFrame operations or vectorized UDFs.
- Do NOT use {{ this }} without {% if is_incremental() %} guard.
- Do NOT set cluster_by on small tables (< 1TB).
- Do NOT use materialized='table' for everything — views are free.
- Do NOT hardcode database/schema — use {{ ref() }} and {{ source() }}.