snowflake-development

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---
name: "snowflake-development"
description: "Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors."
---

# 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](https://github.com/jamescha-earley) — enhanced and integrated by the claude-skills team.

## Quick Start

```bash
# 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.

```sql
-- 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

```sql
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

| 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, complex branching. |
| Snowpipe | Continuous file loading from cloud storage (S3, GCS, Azure). |

### Dynamic Tables

```sql
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

```sql
C