gh-treasure-data-aps-claude-tools-plugins-cdp-staging/commands/transform-validation.md

name, description

name	description
transform-validation	Validate staging transformation files against CLAUDE.md compliance and quality gates

Validate Staging Transformation

⚠️ CRITICAL: This validates against strict production quality gates

I'll validate your staging transformation files for compliance with CLAUDE.md specifications and production standards.

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What I'll Validate

Quality Gates (ALL MUST PASS)

1. File Structure Compliance

• ✅ Correct directory structure (staging/ or staging_hive/)
• ✅ Proper file naming conventions
• ✅ All required files present
• ✅ No missing dependencies

2. SQL File Validation

• ✅ Incremental SQL (staging/queries/{source_db}_{table}.sql)
  • Correct FROM clause with {source_database}.{source_table}
  • Correct WHERE clause with incremental logic
  • Proper database references
• ✅ Initial Load SQL (staging/init_queries/{source_db}_{table}_init.sql)
  • Full table scan (no WHERE clause)
  • Same transformations as incremental
• ✅ Upsert SQL (staging/queries/{source_db}_{table}_upsert.sql)
  • Only present if deduplication exists
  • Correct DELETE and INSERT logic
  • Work table pattern used

3. Data Transformation Standards

• ✅ Column Processing:
  • All columns transformed (except 'time')
  • Column limit compliance (max 200)
  • Proper type casting
• ✅ Date Columns (CRITICAL):
  • ALL date columns have 4 outputs (original, _std, _unixtime, _date)
  • time column NOT transformed
  • Correct FORMAT_DATETIME patterns (Presto) or date_format (Hive)
• ✅ JSON Columns (CRITICAL):
  • ALL JSON columns detected and processed
  • Top-level key extraction completed
  • Nested objects handled (up to 2 levels)
  • Array handling with TRY_CAST
  • All extractions wrapped with NULLIF(UPPER(...), '')
• ✅ Email Columns:
  • Original + _std + _hash + _valid outputs
  • Correct validation regex
  • SHA256 hashing applied
• ✅ Phone Columns:
  • Exact pattern compliance
  • phone_std, phone_hash, phone_valid outputs
  • No deviations from templates

4. Data Processing Order

• ✅ Clean → Join → Dedupe sequence followed
• ✅ Joins use cleaned columns (not raw)
• ✅ Deduplication uses cleaned columns (not raw)
• ✅ No raw column names in PARTITION BY
• ✅ Proper CTE structure

5. Configuration File Validation

• ✅ src_params.yml structure:
  • Valid YAML syntax
  • All required fields present
  • Correct dependency group structure
  • Proper table configuration
• ✅ Table Configuration:
  • name, source_db, staging_table present
  • has_dedup boolean correct
  • partition_columns matches dedup logic
  • mode set correctly (inc/full)

6. DIG File Validation

• ✅ Loop-based architecture used
• ✅ No repetitive table blocks
• ✅ Correct template structure
• ✅ Proper error handling
• ✅ Inc_log table creation
• ✅ Conditional processing logic

7. Treasure Data Compatibility

• ✅ Forbidden Types:
  • No TIMESTAMP types (must be VARCHAR/BIGINT)
  • No BOOLEAN types (must be VARCHAR)
  • No DOUBLE for timestamps
• ✅ Function Compatibility:
  • Presto: FORMAT_DATETIME, TD_TIME_PARSE
  • Hive: date_format, unix_timestamp, from_unixtime
• ✅ Type Casting:
  • TRY_CAST used for safety
  • All timestamps cast to VARCHAR or BIGINT

8. Incremental Processing

• ✅ State Management:
  • Correct inc_log table usage
  • COALESCE(MAX(inc_value), 0) pattern
  • Proper WHERE clause filtering
• ✅ Database References:
  • Source table: {source_database}.{source_table}
  • Inc_log: ${lkup_db}.inc_log
  • Correct project_name filter

9. Engine-Specific Validation

For Presto/Trino:

• ✅ Files in staging/ directory
• ✅ Three SQL files (init, incremental, upsert if dedup)
• ✅ Presto-compatible functions used
• ✅ JSON: json_extract_scalar with NULLIF(UPPER(...), '')

For Hive:

• ✅ Files in staging_hive/ directory
• ✅ Single SQL file with INSERT OVERWRITE
• ✅ Template files present (get_max_time.sql, get_stg_rows_for_delete.sql)
• ✅ Hive-compatible functions used
• ✅ JSON: REGEXP_EXTRACT with NULLIF(UPPER(...), '')
• ✅ Timestamp keyword escaped with backticks

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Validation Options

Option 1: Validate Specific Table

Provide:

• Table Name: e.g., client_src.customer_profiles
• Engine: presto or hive

I will:

1. Find all related files for the table
2. Check against ALL quality gates
3. Report detailed findings with line numbers
4. Provide remediation guidance

Option 2: Validate All Tables

Say: "validate all" or "validate all Presto" or "validate all Hive"

I will:

1. Find all transformation files
2. Validate each table against quality gates
3. Report comprehensive project status
4. Identify any inconsistencies

Option 3: Validate Configuration Only

Say: "validate config"

I will:

1. Check staging/config/src_params.yml (Presto)
2. Check staging_hive/config/src_params.yml (Hive)
3. Validate YAML syntax
4. Verify table configurations
5. Check dependency groups

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Validation Process

Step 1: File Discovery

I will locate all relevant files:

• SQL files (init, queries, upsert)
• Configuration files (src_params.yml)
• Workflow files (staging_transformation.dig or staging_hive.dig)

Step 2: Load and Parse

I will read all files:

• Parse SQL syntax
• Parse YAML structure
• Extract transformation logic
• Identify patterns

Step 3: Quality Gate Checks

I will verify each gate systematically:

• Execute all validation rules
• Identify violations
• Collect evidence (line numbers, code samples)
• Categorize severity

Step 4: Generate Report

Pass Report (if all gates pass)

✅ VALIDATION PASSED

Table: client_src.customer_profiles
Engine: Presto
Files validated: 3

Quality Gates: 9/9 PASSED
✅ File Structure Compliance
✅ SQL File Validation
✅ Data Transformation Standards
✅ Data Processing Order
✅ Configuration File Validation
✅ DIG File Validation
✅ Treasure Data Compatibility
✅ Incremental Processing
✅ Engine-Specific Validation

Transformation Details:
- Columns: 45 (within 200 limit)
- Date columns: 3 (12 outputs - all have 4 outputs ✅)
- JSON columns: 2 (all processed ✅)
- Email columns: 1 (validated with hashing ✅)
- Phone columns: 1 (exact pattern compliance ✅)
- Deduplication: Yes (customer_id)
- Joins: None

No issues found. Transformation is production-ready.

Fail Report (if any gate fails)

❌ VALIDATION FAILED

Table: client_src.customer_profiles
Engine: Presto
Files validated: 3

Quality Gates: 6/9 PASSED

✅ File Structure Compliance
✅ SQL File Validation
❌ Data Transformation Standards - FAILED
  - Date column 'created_at' missing outputs
    Line 45: Only has _std output, missing _unixtime and _date
  - JSON column 'attributes' not processed
    Line 67: Raw column passed through without extraction

✅ Data Processing Order
✅ Configuration File Validation
❌ DIG File Validation - FAILED
  - Using old repetitive block structure
    File: staging/staging_transformation.dig
    Issue: Should use loop-based architecture

✅ Treasure Data Compatibility
✅ Incremental Processing
❌ Engine-Specific Validation - FAILED
  - JSON extraction missing NULLIF(UPPER(...), '') wrapper
    Line 72: json_extract_scalar used without wrapper
    Should be: NULLIF(UPPER(json_extract_scalar(...)), '')

CRITICAL ISSUES (Must Fix):
1. Add missing date outputs for 'created_at' column
2. Process 'attributes' JSON column with key extraction
3. Migrate DIG file to loop-based architecture
4. Wrap all JSON extractions with NULLIF(UPPER(...), '')

RECOMMENDATIONS:
- Re-read CLAUDE.md date processing requirements
- Check JSON column detection logic
- Use DIG template from documentation
- Review JSON extraction patterns

Re-validate after fixing issues.

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Common Issues Detected

Data Transformation Violations

• Missing date column outputs (not all 4)
• JSON columns not processed
• Raw columns used in deduplication
• Phone/email patterns deviated from templates

Configuration Violations

• Invalid YAML syntax
• Missing required fields
• Incorrect dependency group structure
• Table name mismatches

DIG File Violations

• Old repetitive block structure
• Missing error handling
• Incorrect template variables
• No inc_log creation

Engine-Specific Violations

• Presto: Wrong date functions, missing TRY_CAST
• Hive: REGEXP_EXTRACT patterns wrong, timestamp not escaped

Incremental Processing Violations

• Wrong WHERE clause pattern
• Incorrect database references
• Missing COALESCE in inc_log lookup

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Remediation Guidance

For Each Failed Gate:

1. Reference CLAUDE.md - Re-read relevant section
2. Check Examples - Review sample code in documentation
3. Fix Violations - Apply exact patterns from templates
4. Re-validate - Run validation again until passes

Quick Fixes:

Date Columns Missing Outputs:

-- Add all 4 outputs for each date column
column AS column,
FORMAT_DATETIME(...) AS column_std,
TD_TIME_PARSE(...) AS column_unixtime,
SUBSTR(...) AS column_date

JSON Columns Not Processed:

-- Sample data, extract keys, add extractions
NULLIF(UPPER(json_extract_scalar(json_col, '$.key')), '') AS json_col_key

DIG File Old Structure:

• Delete old file
• Use loop-based template from documentation
• Update src_params.yml instead

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Next Steps After Validation

If Validation Passes

✅ Transformation is production-ready:

• Deploy with confidence
• Test workflow execution
• Monitor inc_log for health

If Validation Fails

❌ Fix reported issues:

1. Address critical issues first
2. Apply exact patterns from CLAUDE.md
3. Re-validate until all gates pass
4. DO NOT deploy failing transformations

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Production Quality Assurance

This validation ensures:

• ✅ Transformations work correctly
• ✅ Data quality maintained
• ✅ No data loss or corruption
• ✅ Consistent patterns across tables
• ✅ Maintainable code
• ✅ Compliance with standards

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What would you like to validate?

Options:

1. Validate specific table: Provide table name and engine
2. Validate all tables: Say "validate all"
3. Validate configuration: Say "validate config"