snowflake-data-engineering-cursorrules-prompt-file

// 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 — Continuous file loading from S3/GCS/Azure. // Snowpipe Streaming — Low-latency row-level ingestion via SDK (Java, Python).

// Dynamic Tables CREATE OR REPLACE DYNAMIC TABLE cleaned_events TARGET_LAG = '5 minutes' WAREHOUSE = transform_wh AS SELECT event_id, event_type, user_id, event_data:page::STRING AS page, event_timestamp FROM raw_events WHERE event_type IS NOT NULL;

// Chain for multi-step pipelines: CREATE OR REPLACE DYNAMIC TABLE user_sessions TARGET_LAG = '10 minutes' WAREHOUSE = transform_wh AS SELECT user_id, MIN(event_timestamp) AS session_start, MAX(event_timestamp) AS session_end, COUNT(*) AS event_count FROM cleaned_events GROUP BY user_id;

// TARGET_LAG: freshness target. REFRESH_MODE: AUTO, FULL, or INCREMENTAL. // Manage: ALTER DYNAMIC TABLE ... SET TARGET_LAG / REFRESH / SUSPEND / RESUME.

// Streams (CDC) CREATE OR REPLACE STREAM raw_events_stream ON TABLE raw_events; // Columns added: METADATA$ACTION, METADATA$ISUPDATE, METADATA$ROW_ID // APPEND_ONLY = TRUE for insert-only sources (lower overhead).