pipeline

name	pipeline
description	This skill should be used when designing or reviewing data pipelines — ETL patterns, orchestration, and performance optimization for data workflows. Use when: - "design a data pipeline" - "review this ETL" - "optimize data processing" - "how should I orchestrate this" - "pipeline architecture"
phase_relevance	["design","build"]
archetype_relevance	["*"]

Pipeline Engineering Skill

Design, review, and optimize data pipelines and ETL workflows.

Quick Start

Design a Pipeline

/wicked-garden:data:pipeline design \
  --source "postgres://sales_db" \
  --target "s3://data-lake/sales" \
  --frequency daily

Generates: Architecture diagram, ETL logic, orchestration config, monitoring plan.

Review Existing Pipeline

/wicked-garden:data:pipeline review path/to/pipeline/

Analyzes: Code quality, error handling, performance, maintainability.

Pipeline Patterns

Batch ETL

Use when: Regular scheduled loads, historical processing Pattern: Extract → Transform → Validate → Load Tools: Airflow, Dagster, Prefect

Streaming Pipeline

Use when: Real-time processing, event-driven Pattern: Consume → Transform → Sink Tools: Kafka, Flink, Spark Streaming

Incremental Processing

Use when: Large datasets, only processing changes Pattern: Watermark tracking + Merge/Upsert

Pipeline Design Checklist

Architecture

• Source systems identified and accessible
• Data volume estimated (GB/day)
• Latency requirements clear
• Target schema defined
• Orchestration tool selected

Data Quality

• Schema validation at source
• Null handling strategy
• Duplicate detection
• Business rule validation

Error Handling

• Transient errors: retry with backoff
• Fatal errors: alert and stop
• Invalid records: log separately
• Rollback/recovery strategy

Performance

• Parallelization strategy
• Batch size optimized
• Bottlenecks identified
• Scaling plan documented

Monitoring

• Row counts logged
• Processing duration tracked
• Error rates monitored
• Data freshness SLA defined

Operations

• Backfill procedure documented
• Replay capability implemented
• Config externalized
• Secrets managed securely

Common Issues

Issue	Symptoms	Solution
Fails halfway	Partial data, inconsistent state	Staging + commit pattern
Duplicates	Same data loaded multiple times	Watermarks + idempotency
Slow processing	Misses SLA	Profile and optimize bottlenecks

Integration

• wicked-brain:search: Find pipeline code with wicked-brain:search "dag|pipeline" (FTS5 over indexed code)
• Native tasks: Track pipeline issues via TaskCreate with metadata.event_type="task"
• wicked-brain:memory: Recall pipeline patterns

Best Practices

• Idempotency: Same input → same output, pipelines safely rerunnable
• Observability: Log row counts, track duration, emit metrics, alert on anomalies
• Testing: Unit test transforms, integration test full pipeline, test error scenarios
• Documentation: Clear lineage, versioned schemas, operations runbook

External Integration Discovery

Pipeline engineering can leverage available integrations by capability:

Capability	Discovery Patterns	Provides
Warehouses	snowflake, databricks, bigquery	Query execution, schema access
ETL	airbyte, fivetran, dbt	Pipeline status, model metadata
Observability	monte-carlo, datadog	Data quality metrics

Discover available integrations via capability detection. Fall back to wicked-garden:data:analyze for local file analysis via DuckDB.

Reference

For detailed patterns:

• Output Templates - Design doc, review report templates