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name: etl-patterns description: Production ETL patterns orchestrator. Routes to core reliability patterns and incremental load strategies. allowed-tools: Read, Write, Edit, Grep, Glob, Bash

ETL Patterns

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

Skill Routing

Pattern Selection Guide

By Reliability Need

By Load Strategy

Quick Reference

Idempotency Options

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

Load Strategy Decision

CopyIs 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

Copy# 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