Snowflake Development

Internal Safety Override

• Status: local-only unless explicitly opted into external access.
• Before using network tools, account connectors, browser automation, or APIs, state the destination, data scope, and credential source.
• Do not transmit repository files, secrets, credentials, or private documents by default.
• Audit categories: network, secrets.

Snowflake Development

Snowflake SQL, data pipelines, Cortex AI, and Snowpark Python development. Covers the colon-prefix rule, semi-structured data, MERGE upserts, Dynamic Tables, Streams+Tasks, Cortex AI functions, agent specs, performance tuning, and security hardening.

Originally contributed by James Cha-Earley — enhanced and integrated by the ai-ops-skills team.

Quick Start

# Generate a MERGE upsert template
python scripts/snowflake_query_helper.py merge --target customers --source staging_customers --key customer_id --columns name,email,updated_at

# Generate a Dynamic Table template
python scripts/snowflake_query_helper.py dynamic-table --name cleaned_events --warehouse transform_wh --lag "5 minutes"

# Generate RBAC grant statements
python scripts/snowflake_query_helper.py grant --role analyst_role --database analytics --schemas public,staging --privileges SELECT,USAGE

SQL Best Practices

Naming and Style

• Use snake_case for all identifiers. Avoid double-quoted identifiers -- they force case-sensitive names that require 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, so explicit lists reduce I/O.

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 treats them as column identifiers and raises "invalid identifier" errors.

-- WRONG: missing colon prefix
SELECT name INTO result FROM users WHERE id = p_id;

-- CORRECT: colon prefix on both variable and parameter
SELECT name INTO :result FROM users WHERE id = :p_id;

This applies to DECLARE variables, LET variables, and procedure parameters when used inside SELECT, INSERT, UPDATE, DELETE, or MERGE.

Semi-Structured Data

• VARIANT, OBJECT, ARRAY for JSON/Avro/Parquet/ORC.
• Access nested fields: src:customer.name::STRING. Always cast with ::TYPE.
• VARIANT null vs SQL NULL: JSON null is stored as the string "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());

See references/snowflake_sql_and_pipelines.md for deeper SQL patterns and anti-patterns.

Data Pipelines

Choosing Your Approach

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 the top of the DAG, looser downstream.
• Incremental DTs cannot depend on Full-refresh DTs.
• SELECT * breaks on upstream schema changes -- use explicit column lists.
• Views cannot sit between two Dynamic Tables in the DAG.

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;

See references/snowflake_sql_and_pipelines.md for DT debugging queries and Snowpipe patterns.

Cortex AI

Function Reference

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

TO_FILE -- Common Pitfall

Stage path and filename are separate arguments:

-- WRONG: single combined argument
TO_FILE('@stage/file.pdf')

-- CORRECT: two arguments
TO_FILE('@db.schema.mystage', 'invoice.pdf')

Cortex Agents

Agent specs use a JSON structure with top-level keys: models, instructions, tools, tool_resources.

• Use $spec$ delimiter (not $$).
• models must be an object, not an array.
• tool_resources is a separate top-level key, not nested inside tools.
• Tool descriptions are the single biggest factor in agent quality.

See references/cortex_ai_and_agents.md for full agent spec examples and Cortex Search patterns.

Snowpark Python

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()

• Never hardcode credentials. Use environment variables or key pair auth.
• DataFrames are lazy -- executed on collect() / show().
• Do NOT call collect() on large DataFrames. Process server-side with DataFrame operations.
• Use vectorized UDFs (10-100x faster) for batch and ML workloads.

dbt on Snowflake

-- Dynamic table materialization (streaming/near-real-time marts):
{{ config(materialized='dynamic_table', snowflake_warehouse='transforming', target_lag='1 hour') }}

-- Incremental materialization (large fact tables):
{{ config(materialized='incremental', unique_key='event_id') }}

-- Snowflake-specific configs (combine with any materialization):
{{ config(transient=true, copy_grants=true, query_tag='team_daily') }}

• Do NOT use {{ this }} without {% if is_incremental() %} guard.
• Use dynamic_table materialization for streaming or near-real-time marts.

Performance

• Cluster keys: Only for multi-TB tables. Apply on WHERE / JOIN / GROUP BY columns.
• Search Optimization: ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);
• Warehouse sizing: Start X-Small, scale up. Set AUTO_SUSPEND = 60, AUTO_RESUME = TRUE.
• Separate warehouses per workload (load, transform, query).

Security

• Follow least-privilege RBAC. Use database roles for object-level grants.
• Audit ACCOUNTADMIN regularly: SHOW GRANTS OF ROLE ACCOUNTADMIN;
• Use network policies for IP allowlisting.
• Use masking policies for PII columns and row access policies for multi-tenant isolation.

Proactive Triggers

Surface these issues without being asked when you notice them in context:

• Missing colon prefix in SQL stored procedures -- flag immediately, this causes "invalid identifier" at runtime.
• SELECT * in Dynamic Tables -- flag as a schema-change time bomb.
• Deprecated Cortex function names (CLASSIFY_TEXT, SUMMARIZE, etc.) -- suggest the current AI_* equivalents.
• Task not resumed after creation -- remind that tasks start SUSPENDED.
• Hardcoded credentials in Snowpark code -- flag as a security risk.

Common Errors

Practical Workflows

Workflow 1: Build a Reporting Pipeline (30 min)

1. Stage raw data: Create external stage pointing to S3/GCS/Azure, set up Snowpipe for auto-ingest
2. Clean with Dynamic Table: Create DT with TARGET_LAG = '5 minutes' that filters nulls, casts types, deduplicates
3. Aggregate with downstream DT: Second DT that joins cleaned data with dimension tables, computes metrics
4. Expose via Secure View: Create SECURE VIEW for the BI tool / API layer
5. Grant access: Use snowflake_query_helper.py grant to generate RBAC statements

Workflow 2: Add AI Classification to Existing Data

1. Identify the column: Find the text column to classify (e.g., support tickets, reviews)
2. Test with AI_CLASSIFY: SELECT AI_CLASSIFY(text_col, ['bug', 'feature', 'question']) FROM table LIMIT 10;
3. Create enrichment DT: Dynamic Table that runs AI_CLASSIFY on new rows automatically
4. Monitor costs: Cortex AI is billed per token — sample before running on full tables

Workflow 3: Debug a Failing Pipeline

1. Check task history: SELECT * FROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY()) WHERE STATE = 'FAILED' ORDER BY SCHEDULED_TIME DESC;
2. Check DT refresh: SELECT * FROM TABLE(INFORMATION_SCHEMA.DYNAMIC_TABLE_REFRESH_HISTORY('my_dt')) ORDER BY REFRESH_END_TIME DESC;
3. Check stream staleness: SHOW STREAMS; -- check stale_after column
4. Consult troubleshooting reference: See references/troubleshooting.md for error-specific fixes

Anti-Patterns

Cross-References

Reference Documentation