ETL Pipeline

Extract, Transform, Load pipeline with automated validation and quality checks

Pipeline Stages

📥

1. Extract

Pull data from banking networks using web automation, REST APIs, SOAP, FIX, or ISO 20022

Data Sources

• Clearing houses (ACH, SWIFT, FedWire)
• Payment processors (Visa, Mastercard, PayPal)
• Banking portals (web automation)
• Direct API integrations

Extraction Methods

• Puppeteer/Playwright (web scraping)
• REST API calls with OAuth/JWT
• SOAP web services
• FIX Protocol messages

✓

2. Validate

Verify data quality, schema compliance, and business rules before processing

Schema Validation

• Required field presence checks
• Data type validation (string, number, date)
• Format validation (email, phone, currency)
• Range and constraint validation

Business Rules

• Transaction amount limits
• Date range validation (no future dates)
• Currency code verification (ISO 4217)
• Duplicate detection

🔄

3. Transform

Normalize data to standard formats and apply mapping rules

Normalization

• Date/time standardization (ISO 8601)
• Currency conversion to base currency
• Phone number formatting (E.164)
• Address parsing and geocoding

Field Mapping

• Source to target field mapping
• Calculated fields (fees, totals)
• Enrichment from reference data
• Anonymization/masking for PII

💾

4. Load

Write validated and transformed data to target storage systems

Storage Targets

• PostgreSQL (transactional data)
• Data Warehouse (analytics)
• Dynamic SL (archives and backups)

Load Strategies

• Batch insert with transaction support
• Upsert (insert or update if exists)
• Incremental loading with watermarks
• Parallel loading for performance

Data Validator

Validation Rules

Required Fields

Ensure critical fields are present and non-empty

Type Checking

Validate data types match schema (string, number, boolean, date)

Format Validation

Verify formats (email, URL, phone, credit card)

Range Constraints

Check min/max values and string lengths

Error Handling

Field-Level Errors

Detailed error messages for each invalid field

Error Aggregation

Collect all errors before failing (not fail-fast)

Quarantine Queue

Invalid records moved to quarantine for manual review

Retry Logic

Transient failures retried with exponential backoff

Validation Result Example
{
  "isValid": false,
  "errors": [
    {
      "field": "amount",
      "message": "Amount must be greater than 0",
      "value": -100,
      "rule": "min"
    },
    {
      "field": "email",
      "message": "Invalid email format",
      "value": "invalid-email",
      "rule": "format"
    }
  ],
  "validRecords": 850,
  "invalidRecords": 12,
  "totalRecords": 862
}

Transformation Engine

Type Conversion

• String to Number/Date parsing
• Date format standardization
• Boolean normalization
• Null/undefined handling
• Decimal precision rounding

Field Mapping

• One-to-one field mapping
• Many-to-one aggregation
• One-to-many splitting
• Nested object flattening
• Conditional mapping rules

Data Enrichment

• Lookup from reference tables
• Geocoding addresses
• Currency conversion
• Calculated fields
• Data deduplication

Transformation Configuration Example
{
  "transformations": [
    {
      "type": "fieldMapping",
      "source": "transaction_amt",
      "target": "amount",
      "conversion": "toNumber"
    },
    {
      "type": "dateFormat",
      "source": "txn_date",
      "target": "transactionDate",
      "inputFormat": "MM/DD/YYYY",
      "outputFormat": "ISO8601"
    },
    {
      "type": "calculation",
      "target": "totalWithFees",
      "formula": "amount + processingFee"
    },
    {
      "type": "lookup",
      "source": "bank_code",
      "target": "bankName",
      "referenceTable": "banks",
      "lookupKey": "code",
      "returnField": "name"
    }
  ]
}

Storage Manager

🗄️

PostgreSQL

Primary transactional database for real-time operational data

• ACID transaction guarantees
• Batch inserts with prepared statements
• Connection pooling (pg-pool)
• Automatic retry on deadlocks
• Index optimization for queries

📊

Data Warehouse

Analytical database for reporting and business intelligence

• Star/snowflake schema design
• Columnar storage for analytics
• Incremental ETL with CDC
• Partitioning by date/region
• Materialized views for aggregations

Pipeline Monitoring & Metrics

99.9%

Success Rate

2.3s

Avg Processing Time

10K/min

Record Throughput

0.1%

Error Rate

Real-time Metrics

•Records processed per second
•Pipeline stage durations
•Validation success/failure rates
•Storage write latency
•Queue depth and backlog

WebSocket Events

•pipeline:started - Pipeline execution begins
•pipeline:stage-change - Stage transition events
•pipeline:batch-progress - Batch processing updates
•pipeline:completed - Successful completion
•pipeline:failed - Pipeline errors