Cursor Snowflake 编码规范：来自 PatrickJS/awesome-cursorrules (40k stars) 的 sno，适用于各类文档与内容的智能化处理。
Cursor Snowflake 编码规范：来自 PatrickJS/awesome-cursorrules (40k stars) 的 sno，适用于各类文档与内容的智能化处理。
**文件来源:** PatrickJS/awesome-cursorrules → `rules/snowflake-data-engineering-cursorrules-prompt-file.mdc`
**原仓库:** https://github.com/PatrickJS/awesome-cursorrules
**评分:** ⭐ 仓库 40k stars (社区最权威 Cursor rules 合集)
把 `snowflake-data-engineering-cursorrules-prompt-file.mdc` 这条 Cursor 编码规则打包成可调用的 AI skill,帮你把代码生成统一到一致的标准上。
> Cursor rules for Snowflake SQL, data pipelines (Dynamic Tables, Streams, Tasks, Snowpipe), semi-structured data, Snowflake Postgres, and cost optimization.
globs: **/*
alwaysApply: false
---
// Snowflake Data Engineering
// Comprehensive guidance for SQL, data pipelines, and platform best practices on Snowflake
You are an expert Snowflake data engineer with deep knowledge of the entire platform: SQL, data pipelines (Dynamic Tables, Streams, Tasks, Snowpipe), semi-structured data, Snowflake Postgres, and cost optimization.
// Architecture
// Snowflake separates storage (columnar micro-partitions), compute (elastic virtual warehouses), and services (metadata, security, optimization).
// ═══════════════════════════════════════════
// SQL AND SEMI-STRUCTURED DATA
// ═══════════════════════════════════════════
// Use VARIANT, OBJECT, and ARRAY types for JSON, Avro, Parquet, ORC.
// Access nested fields with colon notation: src:customer.name::STRING
// Cast explicitly: src:price::NUMBER(10,2), src:created_at::TIMESTAMP_NTZ
// Flatten arrays:
// SELECT f.value:name::STRING AS name
// FROM my_table, LATERAL FLATTEN(input => src:items) f;
// Flatten semi-structured into relational columns when data contains dates, numbers as strings, or arrays.
// Avoid mixed types in the same VARIANT field — prevents subcolumnarization.
// VARIANT null vs SQL NULL: JSON null stored as string "null". Use STRIP_NULL_VALUES => TRUE on load.
// SQL Coding Standards
// - snake_case for all identifiers. Avoid quoted identifiers.
// - CTEs over nested subqueries. CREATE OR REPLACE for idempotent DDL.
// - COPY INTO for bulk loading, not INSERT. MERGE for upserts:
// MERGE INTO target t USING source s ON t.id = s.id
// WHEN MATCHED THEN UPDATE SET t.name = s.name
// WHEN NOT MATCHED THEN INSERT (id, name) VALUES (s.id, s.name);
// Stored Procedures — prefix variables with colon : inside SQL statements:
// CREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS
// BEGIN
// LET result STRING;
// SELECT name INTO :result FROM users WHERE id = :p_id;
// RETURN result;
// END;
// ═══════════════════════════════════════════
// PERFORMANCE OPTIMIZATION
// ═══════════════════════════════════════════
// Cluster keys: for very large tables (multi-TB), on WHERE/JOIN/GROUP BY columns.
// ALTER TABLE large_events CLUSTER BY (event_date, region);
// Search Optimization Service: point lookups on high-cardinality columns, substring/regex.
// ALTER TABLE logs ADD SEARCH OPTIMIZATION ON EQUALITY(sender_ip), SUBSTRING(error_message);
// Materialized Views: pre-compute expensive aggregations (single table only).
// Use RESULT_SCAN(LAST_QUERY_ID()) to reuse results. Query tags for attribution:
// ALTER SESSION SET QUERY_TAG = 'etl_daily_load';
// ═══════════════════════════════════════════
// DATA PIPELINES
// ═══════════════════════════════════════════
// Choose Your Approach:
// Dynamic Tables — Declarative. Define the query, Snowflake handles refresh. Best for most pipelines.
// Streams + Tasks — Imperative CDC + scheduling. Best for procedural logic, stored procedure calls.
// Snowpipe
...(完整内容在原仓库)...有问题或建议,在本 skill 下留言。
把 `snowflake-data-engineering-cursorrules-prompt-file.mdc` 这条 Cursor 编码规则打包成可调用的 AI skill,帮你把代码生成统一到一致的标准上。
> Cursor rules for Snowflake SQL, data pipelines (Dynamic Tables, Streams, Tasks, Snowpipe), semi-structured data, Snowflake Postgres, and cost optimization.