data-pipeline-design

Name: data-pipeline-design
Availability: InStock
Author: jota-batuta

Design ETL and ERP data pipelines with quality gates and schema governance.

Updated Feb 20, 2026

Search Tags:#rls #data-quality #data-pipeline #schema-design #multitenancy #erp-integration #etl-design

Ready to run

npx skills add https://github.com/jota-batuta/batuta-dots --skill data-pipeline-design-jota-batuta

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-pipeline-design
Source: https://github.com/jota-batuta/batuta-dots/tree/main/BatutaClaude/skills/data-pipeline-design
Command: npx skills add https://github.com/jota-batuta/batuta-dots --skill data-pipeline-design-jota-batuta

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams spend excessive time designing and validating data pipelines. This skill provides a structured blueprint to design ETL pipelines, ERP integrations, and data processing workflows with clear quality gates.

Core Features & Use Cases

Structured pipeline design: guides source assessment, ERP integration patterns, and data quality rules to ensure reliable data flows.
Schema and governance: defines conventions and security controls (RLS) for multi-tenant environments and auditable batches.
Use Case: design a cross-system ETL that ingests invoices from a Colombian ERP, validates data quality, and loads into a warehouse with lineage.

Quick Start

Use this skill to start a new ETL pipeline design for a Colombian ERP integration.

Frequently Asked Questions about data-pipeline-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an ETL pipeline for ERP integration?▼

To design an ETL pipeline for ERP integration, apply a structured framework covering source assessment, cross-system data flows, and data quality rules. This ensures reliable extraction, transformation, and loading with clear quality gates.

What is the best way to enforce schema conventions and row-level security in a multi-tenant data warehouse?▼

Enforcing schema conventions and row-level security (RLS) in a multi-tenant data warehouse requires defined governance controls and auditable batches. A structured pipeline design sets these conventions to isolate tenant data securely.

How do I set up data quality rules for a cross-system data processing workflow?▼

Setting up data quality rules for a cross-system data processing workflow involves defining validation checks within your ETL design. A structured pipeline framework provides repeatable patterns to validate data quality before loading into the warehouse.

Can I use a structured pipeline design to ingest invoices from a Colombian ERP with lineage tracking?▼

Yes, you can use a structured pipeline design to ingest invoices from a Colombian ERP with lineage tracking. It provides modular source assessment and ERP integration patterns to validate and load data with auditable lineage.

Does ETL design for multi-tenant environments require specific schema governance?▼

Yes, ETL design for multi-tenant environments requires specific schema governance. Implementing defined schema conventions and row-level security (RLS) controls ensures data isolation and reliable cross-system data flows.

Why do I need a structured framework for data pipeline design instead of building ad-hoc ETL?▼

You need a structured framework for data pipeline design because ad-hoc ETL lacks quality gates. A repeatable blueprint ensures modular source assessment, enforced schema conventions, and validated data quality for reliable ERP integrations.