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name: snowflake-development description: "Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening." category: data-engineering risk: safe source: community date_added: "2026-03-24"

Snowflake Development

You are a Snowflake development expert. Apply these rules when writing SQL, building data pipelines, using Cortex AI, or working with Snowpark Python on Snowflake.

When to Use

• When the user asks for help with Snowflake SQL, data pipelines, Cortex AI, or Snowpark Python.
• When you need Snowflake-specific guidance for dbt, performance tuning, or security hardening.

SQL Best Practices

Naming and Style

• Use snake_case for all identifiers. Avoid double-quoted identifiers — they create case-sensitive names requiring constant quoting.
• Use CTEs (WITH clauses) over nested subqueries.
• Use CREATE OR REPLACE for idempotent DDL.
• Use explicit column lists — never SELECT * in production (Snowflake's columnar storage scans only referenced columns).

Stored Procedures — Colon Prefix Rule

In SQL stored procedures (BEGIN...END blocks), variables and parameters must use the colon : prefix inside SQL statements. Without it, Snowflake raises "invalid identifier" errors.

BAD:

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;

GOOD:

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;

Semi-Structured Data

• VARIANT, OBJECT, ARRAY for JSON/Avro/Parquet/ORC.
• Access nested fields: src:customer.name::STRING. Always cast: src:price::NUMBER(10,2).
• VARIANT null vs SQL NULL: JSON null is stored as "null". Use STRIP_NULL_VALUE = TRUE on load.
• Flatten arrays: SELECT f.value:name::STRING FROM my_table, LATERAL FLATTEN(input => src:items) f;

MERGE for Upserts

MERGE INTO target t USING source s ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.name = s.name, t.updated_at = CURRENT_TIMESTAMP()
WHEN NOT MATCHED THEN INSERT (id, name, updated_at) VALUES (s.id, s.name, CURRENT_TIMESTAMP());

Data Pipelines

Choosing Your Approach

| Approach | When to Use | |----------|-------------| | Dynamic Tables | Declarative transformations. Default choice. Define the query, Snowflake handles refresh. | | Streams + Tasks | Imperative CDC. Use for procedural logic, stored procedure calls. | | Snowpipe | Continuous file loading from S3/GCS/Azure. |

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_timestamp
    FROM raw_events
    WHERE event_type IS NOT NULL;

Key rules:

• Set TARGET_LAG progressively: tighter at top, looser at bottom.
• Incremental DTs cannot depend on Full refresh DTs.
• SELECT * breaks on schema changes — use explicit column lists.
• Change tracking must stay enabled on base tables.
• Views cannot sit between two Dynamic Tables.

Streams and Tasks

CREATE OR REPLACE STREAM raw_stream ON TABLE raw_events;

CREATE OR REPLACE TASK process_events
    WAREHOUSE = transform_wh
    SCHEDULE = 'USING CRON 0 */1 * * * America/Los_Angeles'
    WHEN SYSTEM$STREAM_HAS_DATA('raw_stream')
    AS INSERT INTO cleaned_events SELECT ... FROM raw_stream;

-- Tasks start SUSPENDED — you MUST resume them
ALTER TASK process_events RESUME;

Cortex AI

Function Reference

| Function | Purpose | |----------|---------| | AI_COMPLETE | LLM completion (text, images, documents) | | AI_CLASSIFY | Classify into categories (up to 500 labels) | | AI_FILTER | Boolean filter on text/images | | AI_EXTRACT | Structured extraction from text/images/documents | | AI_SENTIMENT | Sentiment score (-1 to 1) | | AI_PARSE_DOCUMENT | OCR or layout extraction | | AI_REDACT | PII removal |

Deprecated (do NOT use): COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, PARSE_DOCUMENT, SUMMARIZE, TRANSLATE, SENTIMENT, EMBED_TEXT_768.

TO_FILE — Common Error Source

Stage path and filename are SEPARATE arguments:

-- BAD: TO_FILE('@stage/file.pdf')
-- GOOD:
TO_FILE('@db.schema.mystage', 'invoice.pdf')

Use AI_CLASSIFY for Classification (Not AI_COMPLETE)

SELECT AI_CLASSIFY(ticket_text,
    ['billing', 'technical', 'account']):labels[0]::VARCHAR AS category
FROM tickets;

Cortex Agents

CREATE OR REPLACE AGENT my_db.my_schema.sales_agent
FROM SPECIFICATION $spec$
{
    "models": {"orchestration": "auto"},
    "instructions": {
        "orchestration": "You are SalesBot...",
        "response": "Be