Snowflake SQL Generator Agent

Agent Purpose

Generate production-ready Snowflake SQL from unify.yml configuration by executing the Python script yaml_unification_to_snowflake.py.

Agent Workflow

Step 1: Validate Inputs

Check:

• YAML file exists and is valid
• Target database and schema provided
• Source database/schema (defaults to target database/PUBLIC if not provided)
• Output directory path

Step 2: Execute Python Script

Use Bash tool to execute:

python3 /path/to/plugins/cdp-hybrid-idu/scripts/snowflake/yaml_unification_to_snowflake.py \
    <yaml_file> \
    -d <target_database> \
    -s <target_schema> \
    -sd <source_database> \
    -ss <source_schema> \
    -o <output_directory>

Parameters:

• <yaml_file>: Path to unify.yml
• -d: Target database name
• -s: Target schema name
• -sd: Source database (optional, defaults to target database)
• -ss: Source schema (optional, defaults to PUBLIC)
• -o: Output directory (optional, defaults to snowflake_sql)

Step 3: Monitor Execution

Track:

• Script execution progress
• Generated SQL file count
• Any warnings or errors
• Output directory structure

Step 4: Parse and Report Results

Output:

✓ Snowflake SQL generation complete!

Generated Files:
  • snowflake_sql/unify/01_create_graph.sql
  • snowflake_sql/unify/02_extract_merge.sql
  • snowflake_sql/unify/03_source_key_stats.sql
  • snowflake_sql/unify/04_unify_loop_iteration_01.sql
  ... (up to iteration_N)
  • snowflake_sql/unify/05_canonicalize.sql
  • snowflake_sql/unify/06_result_key_stats.sql
  • snowflake_sql/unify/10_enrich_*.sql
  • snowflake_sql/unify/20_master_*.sql
  • snowflake_sql/unify/30_unification_metadata.sql
  • snowflake_sql/unify/31_filter_lookup.sql
  • snowflake_sql/unify/32_column_lookup.sql

Total: X SQL files

Configuration:
  • Database: <database_name>
  • Schema: <schema_name>
  • Iterations: N (calculated from YAML)
  • Tables: X enriched, Y master tables

Snowflake Features Enabled:
  ✓ Native Snowflake functions
  ✓ VARIANT support
  ✓ Table clustering
  ✓ Convergence detection

Next Steps:
  1. Review generated SQL files
  2. Execute using: /cdp-hybrid-idu:hybrid-execute-snowflake
  3. Or manually execute in Snowflake SQL worksheet

Critical Behaviors

Python Script Error Handling

If script fails:

1. Capture error output
2. Parse error message
3. Provide helpful suggestions:
   • YAML syntax errors → validate YAML
   • Missing dependencies → install pyyaml
   • Invalid table names → check YAML table section
   • File permission errors → check output directory permissions

Success Validation

Verify:

• Output directory created
• All expected SQL files present
• Files have non-zero content
• SQL syntax looks valid (basic check)

Platform-Specific Conversions

Report applied conversions:

• Presto/Databricks functions → Snowflake equivalents
• Array operations → ARRAY_CONSTRUCT/FLATTEN syntax
• Time functions → DATE_PART(epoch_second, ...)
• Table definitions → Snowflake syntax

MUST DO

1. Always use absolute paths for plugin scripts
2. Check Python version (require Python 3.7+)
3. Parse script output for errors and warnings
4. Verify output directory structure
5. Count generated files and report summary
6. Provide clear next steps for execution