Data

The following section design a comprehensive data architecture for an enterprise payment system handling digital payment tokens and mobile payments.

Let me explain the key components and how to structure them:

1. Operational Data (Hot Storage)

   • Transaction Data:

     • Core fields: transaction_id, amount, currency, timestamp, status

     • Payment method details (Google Pay/Apple Pay tokens)

     • Should be highly available and quickly accessible

     • Retention: Live system 90-180 days

   • Token Data:

     • Tokenized payment credentials

     • Token metadata (expiry, token provider)

     • Requires encryption at rest

     • Retention: As long as token is active

   • Wallet Data:

     • User payment preferences

     • Linked payment methods

     • Token-to-card mappings

     • Retention: Account lifetime + 30 days

2. Analytical Data (Warm Storage)

   • Aggregated metrics:

     • Daily/weekly/monthly transaction volumes

     • Success rates by payment method

     • Average transaction values

     • Retention: 3-5 years

   • Time series data:

     • Transaction patterns

     • Usage trends

     • Performance metrics

     • Retention: 1-2 years

3. Reference Data

   • Payment method configurations

   • Fee structures

   • Merchant categories

   • Retention: Keep historical versions

4. Archive Data (Cold Storage)

   • Completed transactions

   • Inactive tokens

   • Closed accounts

   • Retention: 7-10 years (based on regulatory requirements)

For data documentation and lineage:

1. Data Dictionary:

- Each data element should have:
  - Technical name
  - Business description
  - Data type and format
  - Source system
  - Security classification
  - Retention period
  - Regulatory requirements

1. Data Lineage Tracking:

   • Use tools like Collibra or Apache Atlas

   • Track:

     • Data source origins

     • Transformation steps

     • Dependencies

     • Usage patterns

     • Access history

2. Metadata Management:

   • Version control for schema changes

   • Data quality metrics

   • Access controls and audit logs

   • Business context and ownership

Best Practices:

1. Data Classification:

   • PCI data (highly sensitive)

   • Personal data (GDPR/CCPA scope)

   • Business data (internal use)

   • Public data

2. Storage Considerations:

   • Hot data: High-performance databases (PostgreSQL, MongoDB)

   • Warm data: Data warehouses (Snowflake, BigQuery)

   • Cold data: Object storage (S3, Google Cloud Storage)

3. Security Requirements:

   • Encryption at rest and in transit

   • Tokenization for sensitive data

   • Access control and audit logging

   • Data masking for non-production environments

Would you like me to elaborate on any of these aspects or provide more specific details about a particular component?