Unify Configuration Creator for Snowflake/Databricks

Overview

I'll automatically generate a production-ready unify.yml configuration file for your Snowflake or Databricks ID unification by:

1. Analyzing your actual tables using platform-specific MCP tools
2. Extracting user identifiers with zero-tolerance PII detection
3. Validating data patterns from real table data
4. Generating unify.yml using the exact template format
5. Providing recommendations for merge strategies and priorities

This command uses STRICT analysis - only tables with actual user identifiers will be included.

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What You Need to Provide

1. Platform Selection

• Snowflake: For Snowflake databases
• Databricks: For Databricks Unity Catalog tables

2. Tables to Analyze

Provide tables you want to analyze for ID unification:

• Format (Snowflake): database.schema.table or schema.table or table
• Format (Databricks): catalog.schema.table or schema.table or table
• Example: customer_data.public.customers, orders, web_events.user_activity

3. Canonical ID Configuration

• Name: Name for your unified ID (default: td_id)
• Merge Iterations: Number of unification loop iterations (default: 10)
• Incremental Iterations: Iterations for incremental processing (default: 5)

4. Output Configuration (Optional)

• Output File: Where to save unify.yml (default: unify.yml)
• Template Path: Path to template if using custom (default: uses built-in exact template)

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What I'll Do

Step 1: Platform Detection and Validation

1. Confirm platform (Snowflake or Databricks)
2. Verify MCP tools are available for the platform
3. Set up platform-specific query patterns
4. Inform you of the analysis approach

Step 2: Key Extraction with hybrid-unif-keys-extractor Agent

I'll launch the hybrid-unif-keys-extractor agent to:

Schema Analysis:

• Use platform MCP tools to describe each table
• Extract exact column names and data types
• Identify accessible vs inaccessible tables

User Identifier Detection:

• Apply STRICT matching rules for user identifiers:
  • ✅ Email columns (email, email_std, email_address, etc.)
  • ✅ Phone columns (phone, phone_number, mobile_phone, etc.)
  • ✅ User IDs (user_id, customer_id, account_id, etc.)
  • ✅ Cookie/Device IDs (td_client_id, cookie_id, etc.)
  • ❌ System columns (id, created_at, time, etc.)
  • ❌ Complex types (arrays, maps, objects, variants, structs)

Data Validation:

• Query actual MIN/MAX values from each identified column
• Analyze data patterns and quality
• Count unique values per identifier
• Detect data quality issues

Table Classification:

• INCLUDED: Tables with valid user identifiers
• EXCLUDED: Tables without user identifiers (fully documented why)

Expert Analysis:

• 3 SQL experts review the data
• Provide priority recommendations
• Suggest validation rules based on actual data patterns

Step 3: Unify.yml Generation

CRITICAL: Using the EXACT BUILT-IN template structure (embedded in hybrid-unif-keys-extractor agent)

Template Usage Process:

1. Receive structured data from hybrid-unif-keys-extractor agent:
   - Keys with validation rules
   - Tables with column mappings
   - Canonical ID configuration
   - Master tables specification

2. Use BUILT-IN template structure (see agent documentation)

3. ONLY replace these specific values:
   - Line 1: name: {canonical_id_name}
   - keys section: actual keys found
   - tables section: actual tables with actual columns
   - canonical_ids section: name and merge_by_keys
   - master_tables section: [] or user specifications

4. PRESERVE everything else:
   - ALL comment blocks (#####...)
   - ALL comment text ("Declare Validation logic", etc.)
   - ALL spacing and indentation (2 spaces per level)
   - ALL blank lines
   - EXACT YAML structure

5. Use Write tool to save populated unify.yml

I'll generate:

Section 1: Canonical ID Name

name: {your_canonical_id_name}

Section 2: Keys with Validation

keys:
  - name: email
    valid_regexp: ".*@.*"
    invalid_texts: ['', 'N/A', 'null']
  - name: customer_id
    invalid_texts: ['', 'N/A', 'null']
  - name: phone_number
    invalid_texts: ['', 'N/A', 'null']

Populated with actual keys found in your tables

Section 3: Tables with Key Column Mappings

tables:
  - database: {database/catalog}
    table: {table_name}
    key_columns:
      - {column: actual_column_name, key: mapped_key}
      - {column: another_column, key: another_key}

Only tables with valid user identifiers, with EXACT column names from schema analysis

Section 4: Canonical IDs Configuration

canonical_ids:
  - name: {your_canonical_id_name}
    merge_by_keys: [email, customer_id, phone_number]
    merge_iterations: 15

Based on extracted keys and your configuration

Section 5: Master Tables (Optional)

master_tables:
  - name: {canonical_id_name}_master_table
    canonical_id: {canonical_id_name}
    attributes:
      - name: best_email
        source_columns:
          - {table: table1, column: email, order: last, order_by: time, priority: 1}
          - {table: table2, column: email_address, order: last, order_by: time, priority: 2}

If you request master table configuration, I'll help set up attribute aggregation

Step 4: Validation and Review

After generation:

1. Show complete unify.yml content
2. Highlight key sections:
   - Keys found: [list]
   - Tables included: [count]
   - Tables excluded: [count] with reasons
   - Merge strategy: [keys and priorities]
3. Provide recommendations for optimization
4. Ask for your approval before saving

Step 5: File Output

1. Write unify.yml to specified location
2. Create backup of existing file if present
3. Provide file summary:
   - Keys configured: X
   - Tables configured: Y
   - Validation rules: Z
4. Show next steps for using the configuration

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Example Workflow

Input:

Platform: Snowflake
Tables:
  - customer_data.public.customers
  - customer_data.public.orders
  - web_data.public.events
Canonical ID Name: unified_customer_id
Output: snowflake_unify.yml

Process:

✓ Platform: Snowflake MCP tools detected
✓ Analyzing 3 tables...

Schema Analysis:
  ✓ customer_data.public.customers - 12 columns
  ✓ customer_data.public.orders - 8 columns
  ✓ web_data.public.events - 15 columns

User Identifier Detection:
  ✓ customers: email, customer_id (2 identifiers)
  ✓ orders: customer_id, email_address (2 identifiers)
  ✗ events: NO user identifiers found
    Available columns: event_id, session_id, page_url, timestamp, ...
    Reason: Contains only event tracking data - no PII

Data Analysis:
  ✓ email: 45,123 unique values, format valid
  ✓ customer_id: 45,089 unique values, numeric
  ✓ email_address: 12,456 unique values, format valid

Expert Analysis Complete:
  Priority 1: customer_id (most stable, highest coverage)
  Priority 2: email (good coverage, some quality issues)
  Priority 3: phone_number (not found)

Generating unify.yml...
  ✓ Keys section: 2 keys configured
  ✓ Tables section: 2 tables configured
  ✓ Canonical IDs: unified_customer_id
  ✓ Validation rules: Applied based on data patterns

Tables EXCLUDED:
  - web_data.public.events: No user identifiers

Output (snowflake_unify.yml):

name: unified_customer_id

keys:
  - name: email
    valid_regexp: ".*@.*"
    invalid_texts: ['', 'N/A', 'null']
  - name: customer_id
    invalid_texts: ['', 'N/A', 'null']

tables:
  - database: customer_data
    table: customers
    key_columns:
      - {column: email, key: email}
      - {column: customer_id, key: customer_id}
  - database: customer_data
    table: orders
    key_columns:
      - {column: email_address, key: email}
      - {column: customer_id, key: customer_id}

canonical_ids:
  - name: unified_customer_id
    merge_by_keys: [customer_id, email]
    merge_iterations: 15

master_tables: []

---

Key Features

🔍 STRICT PII Detection

• Zero tolerance for guessing
• Only includes tables with actual user identifiers
• Documents why tables are excluded
• Based on REAL schema and data analysis

✅ Exact Template Compliance

• Uses BUILT-IN exact template structure (embedded in hybrid-unif-keys-extractor agent)
• NO modifications to template format
• Preserves all comment sections
• Maintains exact YAML structure
• Portable across all systems

📊 Real Data Analysis

• Queries actual MIN/MAX values
• Counts unique identifiers
• Validates data patterns
• Identifies quality issues

🎯 Platform-Aware

• Uses correct MCP tools for each platform
• Respects platform naming conventions
• Applies platform-specific data type rules
• Generates platform-compatible SQL references

📋 Complete Documentation

• Documents all excluded tables with reasons
• Lists available columns for excluded tables
• Explains why columns don't qualify as user identifiers
• Provides expert recommendations

---

Output Format

The generated unify.yml will have EXACTLY this structure:

name: {canonical_id_name}
#####################################################
##
##Declare Validation logic for unification keys
##
#####################################################
keys:
  - name: {key1}
    valid_regexp: "{pattern}"
    invalid_texts: ['{val1}', '{val2}', '{val3}']
  - name: {key2}
    invalid_texts: ['{val1}', '{val2}', '{val3}']

#####################################################
##
##Declare databases, tables, and keys to use during unification
##
#####################################################

tables:
  - database: {db/catalog}
    table: {table}
    key_columns:
      - {column: {col}, key: {key}}

#####################################################
##
##Declare hierarchy for unification. Define keys to use for each level.
##
#####################################################

canonical_ids:
  - name: {canonical_id_name}
    merge_by_keys: [{key1}, {key2}, ...]
    merge_iterations: {number}

#####################################################
##
##Declare Similar Attributes and standardize into a single column
##
#####################################################

master_tables:
  - name: {canonical_id_name}_master_table
    canonical_id: {canonical_id_name}
    attributes:
      - name: {attribute}
        source_columns:
          - {table: {t}, column: {c}, order: last, order_by: time, priority: 1}

NO deviations from this structure - EXACT template compliance guaranteed.

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Prerequisites

Required:

• ✅ Snowflake or Databricks platform access
• ✅ Platform-specific MCP tools configured (may use fallback if unavailable)
• ✅ Read permissions on tables to be analyzed
• ✅ Tables must exist and be accessible

Optional:

• Custom unify.yml template path (if not using default)
• Master table attribute specifications
• Custom validation rules

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Expected Timeline

Step	Duration
Platform detection	< 1 min
Schema analysis (per table)	5-10 sec
Data analysis (per identifier)	10-20 sec
Expert analysis	1-2 min
YAML generation	< 1 min
Total (for 5 tables)	~3-5 min

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Error Handling

Common Issues:

Issue: MCP tools not available for platform Solution:

• I'll inform you and provide fallback options
• You can provide schema information manually
• I'll still generate unify.yml with validation warnings

Issue: No tables have user identifiers Solution:

• I'll show you why tables were excluded
• Suggest alternative tables to analyze
• Explain what constitutes a user identifier

Issue: Table not accessible Solution:

• Document which tables are inaccessible
• Continue with accessible tables
• Recommend permission checks

Issue: Complex data types found Solution:

• Exclude complex type columns (arrays, structs, maps)
• Explain why they can't be used for unification
• Suggest alternative columns if available

---

Success Criteria

Generated unify.yml will:

• ✅ Use EXACT template structure - NO modifications
• ✅ Contain ONLY tables with validated user identifiers
• ✅ Include ONLY columns that actually exist in tables
• ✅ Have validation rules based on actual data patterns
• ✅ Be ready for immediate use with hybrid-generate-snowflake or hybrid-generate-databricks
• ✅ Work without any manual edits
• ✅ Include comprehensive documentation in comments

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Next Steps After Generation

1. Review the generated unify.yml

   • Verify tables and columns are correct
   • Check validation rules are appropriate
   • Review merge strategy and priorities

2. Generate SQL for your platform:

   • Snowflake: /cdp-hybrid-idu:hybrid-generate-snowflake
   • Databricks: /cdp-hybrid-idu:hybrid-generate-databricks

3. Execute the workflow:

   • Snowflake: /cdp-hybrid-idu:hybrid-execute-snowflake
   • Databricks: /cdp-hybrid-idu:hybrid-execute-databricks

4. Monitor convergence and results

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Getting Started

Ready to begin?

Please provide:

1. Platform: Snowflake or Databricks
2. Tables: List of tables to analyze (full paths)
3. Canonical ID Name: Name for your unified ID (e.g., unified_customer_id)
4. Output File (optional): Where to save unify.yml (default: unify.yml)

Example:

Platform: Snowflake
Tables:
  - customer_db.public.customers
  - customer_db.public.orders
  - marketing_db.public.campaigns
Canonical ID: unified_id
Output: snowflake_unify.yml

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I'll analyze your tables and generate a production-ready unify.yml configuration!