snowflake-development

# 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:
```sql
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:
```sql
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

```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());
```

## 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

```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 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

```sql
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:

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

### Use AI_CLASSIFY for Classification (Not AI_COMPLETE)

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

### Cortex Agents

```sql
CREATE OR REPLACE AGENT my_db.my_schema.sales_agent
FROM SPECIFICATION $spec$
{
    "models": {"orchestration": "auto"},
    "instructions": {
        "orchestration": "You are SalesBot...",
        "response": "Be concise."
    },
    "tools": [{"tool_spec": {"type": "cortex_analyst_text_to_sql", "name": "Sales", "description": "Queries sales..."}}],
    "tool_resources": {"Sales": {"semantic_model_file": "@stage/model.yaml"}}
}
$spec$;
```

Agent rules:
- Use `$spec$` delimiter (not `$$`).
- `models` must be an object, not an array.
- `tool_resources` is a separate top-level object, not nested inside tools.
- Do NOT include empty/null values in edit specs — clears existing values.
- Tool descriptions are the #1 quality factor.
- Never modify production agents directly — clone first.

## Snowpark Python

```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.
- DataFrames are lazy — executed on `collect()`/`show()`.
- Do NOT use `collect()` on large DataFrames — process server-side.
- Use **vectorized UDFs** (10-100x faster) for batch/ML workloads instead of scalar UDFs.

## dbt on Snowflake

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

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

Snowflake-specific configs (combine with any materialization):
```sql
{{ 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/near-real-time marts.

## Performance

- **Cluster keys**: Only multi-TB tables, on WHERE/JOIN/GROUP BY columns.
- **Search Optimization**: `ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);`
- **Warehouse sizing**: Start X-Small, scale up. `AUTO_SUSPEND = 60`, `AUTO_RESUME = TRUE`.
- **Separate warehouses** per workload.
- Estimate AI costs first: `SELECT SUM(AI_COUNT_TOKENS('claude-4-sonnet', text)) FROM table;`

## 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.

## Common Error Patterns

| Error | Cause | Fix |
|-------|-------|-----|
| "Object does not exist" | Wrong context or missing grants | Fully qualify names, check grants |
| "Invalid identifier" in proc | Missing colon prefix | Use `:variable_name` |
| "Numeric value not recognized" | VARIANT not cast | `src:field::NUMBER(10,2)` |
| Task not running | Forgot to resume | `ALTER TASK ... RESUME` |
| DT refresh failing | Schema change or tracking disabled | Use explicit columns, check change tracking |

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.