etl-patterns

ETL Patterns

Orchestrator for production-grade Extract-Transform-Load patterns.

Skill Routing

Need	Skill	Content
Reliability patterns	etl-core-patterns	Idempotency, checkpointing, error handling, chunking, retry, logging
Load strategies	etl-incremental-patterns	Backfill, timestamp-based, CDC, pipeline orchestration

Pattern Selection Guide

By Reliability Need

Need	Pattern	Skill
Repeatable runs	Idempotency	etl-core-patterns
Resume after failure	Checkpointing	etl-core-patterns
Handle bad records	Error handling + DLQ	etl-core-patterns
Memory management	Chunked processing	etl-core-patterns
Network resilience	Retry with backoff	etl-core-patterns
Observability	Structured logging	etl-core-patterns

By Load Strategy

Scenario	Pattern	Skill
Small tables (<100K)	Full refresh	etl-incremental-patterns
Large tables	Timestamp incremental	etl-incremental-patterns
Real-time sync	CDC events	etl-incremental-patterns
Historical migration	Parallel backfill	etl-incremental-patterns
Zero-downtime refresh	Swap pattern	etl-incremental-patterns
Multi-step pipelines	Pipeline orchestration	etl-incremental-patterns

Quick Reference

Idempotency Options

# Small datasets: Delete-then-insert
# Large datasets: UPSERT on conflict
# Change detection: Row hash comparison

Load Strategy Decision

Is table < 100K rows?
  → Full refresh

Has reliable timestamp column?
  → Timestamp incremental

Source supports CDC?
  → CDC event processing

Need zero downtime?
  → Swap pattern (temp table → rename)

One-time historical load?
  → Parallel backfill with date ranges

Common Pipeline Structure

# 1. Setup
checkpoint = Checkpoint('.etl_checkpoint.json')
processor = ETLProcessor()

# 2. Extract (with incremental)
df = incremental_by_timestamp(source_table, 'updated_at')

# 3. Transform (with error handling)
transformed = processor.process_batch(df.to_dict('records'))

# 4. Load (with idempotency)
upsert_records(pd.DataFrame(transformed))

# 5. Checkpoint
checkpoint.set_last_processed('sync', df['updated_at'].max())

# 6. Handle failures
processor.save_failures('failures/')

Related Skills

• data-validation - Validate data quality during ETL
• data-quality - Monitor data quality metrics
• pandas-coder - DataFrame transformations