Data Pipeline
A real data ingestion pipeline that fetches, validates, transforms, and stores structured data. Built with Python, Pydantic schemas, retry logic, dead letter queues, and full audit logging. Used in Market Snapshot and GlobeScraper projects.
Pattern
ETL pipeline
Validation
Schema + runtime
Monitoring
Structured logging
Retry
Exponential backoff
How it works
Pipeline Architecture
Six-stage model where each stage is a pure function taking input and producing output — testable, retryable, debuggable. Every record validated against a Pydantic model before entering the pipeline. Catches malformed data early with clear error messages.
Resilience Patterns
Exponential backoff retry (1s → 2s → 4s → 8s, max 3 retries). Only retries transient errors (timeouts, 5xx); permanent failures (4xx) skip immediately. Dead letter queue: invalid records written to dead_letters.jsonl with original data, error message, batch ID, and timestamp.
Audit & Versioning
Every run logged with full metadata: batch ID, source, start/end time, duration, record counts (fetched, valid, rejected), output location, status. Datasets versioned by date. Previous versions retained 30 days, then archived.
Security
- Schema validation catches malformed data at ingestion
- Retry handling for transient failures only
- Dead letter queue preserves failed records for reprocessing
- Structured logging with batch IDs for auditability
- Dataset versioning for rollback capability
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