Etl pipeline design

Skill shinzoxD/knackbox/skills/data/etl-pipeline-design

Design ETL and ELT pipelines with clear contracts, quality checks, and failure modes. Use whenever the user plans data ingestion, transformations, warehouse loads, dbt-style models, batch/stream pipelines, or data pipeline architecture.From its SKILL.md

Install

npx -y skills add shinzoxD/knackbox --skill etl-pipeline-design

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SKILL.md

1.7 KB, 291 tokens by cl100k_base, as published. Nobody here has run it

ETL / ELT Pipeline Design

Pipelines are products: contracts, SLAs, and tests. Prefer explicit schemas and idempotent loads over heroic one-off scripts.

Workflow

Sources, destinations, latency needs (batch vs stream).
Grain and business keys; late/duplicate data policy.
ELT vs ETL trade-off for this stack.
Stages: extract, land, validate, transform, publish.
Data quality checks and alerting.
Backfills, reprocessing, and cost controls.
Ownership and lineage docs.

Output format

## Pipeline design: <name>

**SLA / freshness:** …
**Grain:** …

### Architecture
…

### Contracts
…

### Quality checks
…

### Failure & replay
…

### Cost notes
…

Rules

Define idempotency keys for loads.
Never silently drop rows; quarantine or metric the drops.
PII handling and retention called out.
Separate raw/landing from curated marts.
Backfills must not corrupt current tables without plan.
Do not invent warehouse features; mark assumptions.

Edge cases

CDC/streaming: ordering, exactly-once illusions, watermarking.
Slowly changing dimensions: SCD strategy explicit.
Multi-source joins: source-of-truth hierarchy.

What ships with it: 1 file

1.2 KB alongside SKILL.md

benchmarks/

prompts.json1.2 KB