# PRD: Data Pipeline

## Overview
An ETL data pipeline that ingests data from multiple sources, transforms it through configurable processing steps, and loads results into a target data store with monitoring and error recovery.

## Target Users
- Data engineers building batch or streaming pipelines
- Analysts automating data transformation workflows
- Teams consolidating data from multiple sources

## Core Features
1. **Multi-Source Ingestion** - Read from CSV files, JSON APIs, PostgreSQL databases, and S3 buckets
2. **Configurable Transforms** - Chain transformation steps: filter, map, aggregate, join, and deduplicate
3. **Schema Validation** - Validate incoming data against defined schemas, quarantine invalid records
4. **Incremental Processing** - Track watermarks for incremental loads, skip already-processed records
5. **Error Recovery** - Dead letter queue for failed records, automatic retry with configurable policies
6. **Pipeline Monitoring** - Metrics for records processed, errors, throughput, and pipeline duration
7. **Scheduling** - Cron-based scheduling with dependency management between pipeline stages

## Technical Requirements
- Python 3.10+ with type hints
- Pydantic for schema validation
- SQLAlchemy for database connections
- Click for CLI interface
- YAML pipeline definitions
- SQLite for pipeline state and metadata
- Structured JSON logging

## Quality Gates
- Unit tests for each transform function
- Integration tests with sample datasets
- Schema validation tested with valid and invalid records
- Incremental processing verified across multiple runs
- Dead letter queue captures all failure categories

## Project Structure
```
/
├── src/
│   ├── pipeline/
│   │   ├── runner.py          # Pipeline execution engine
│   │   ├── transforms.py      # Built-in transform functions
│   │   └── validators.py      # Schema validation logic
│   ├── sources/
│   │   ├── csv_source.py      # CSV file reader
│   │   ├── json_source.py     # JSON API reader
│   │   └── db_source.py       # PostgreSQL reader
│   ├── sinks/
│   │   └── loader.py          # Target data store writer
│   ├── monitoring/
│   │   └── metrics.py         # Processing metrics collector
│   ├── cli.py                 # Click CLI entrypoint
│   └── config.py              # YAML config loader
├── pipelines/
│   └── example.yaml           # Sample pipeline definition
├── tests/
│   ├── test_transforms.py     # Transform function tests
│   ├── test_validators.py     # Schema validation tests
│   └── test_pipeline.py       # Integration tests
├── pyproject.toml
└── README.md
```

## Out of Scope
- Real-time streaming (Kafka, Kinesis)
- Distributed processing (Spark, Dask)
- Web-based pipeline builder UI
- Data lineage or provenance tracking
- Alerting integrations (PagerDuty, Slack)
- Cloud-native deployment (Airflow, Dagster)
- Data catalog or discovery features

## Acceptance Criteria
- Pipeline definition in YAML configures sources, transforms, and sinks
- CSV, JSON, and database sources each read data correctly
- Invalid records are routed to the dead letter queue with error details
- Incremental mode skips previously processed records on re-run
- Metrics report records processed, errors, and elapsed time
- Cron scheduling triggers pipeline runs at configured intervals

## Success Metrics
- Pipeline processes sample dataset end-to-end without errors
- Invalid records quarantined with descriptive error messages
- Incremental runs process only new records
- Metrics accurately reflect processing statistics
- Pipeline resumes correctly after interruption
- All tests pass

---

**Purpose:** Tests Loki Mode's ability to build a data engineering pipeline with multi-source ingestion, configurable transforms, error recovery, and monitoring. Expect ~30-45 minutes for full execution.