dev-data

What problem does it solve?

This Skill provides a comprehensive guide to building robust and scalable data engineering pipelines, ensuring data quality and efficient processing.

Core Features & Use Cases

• Data Processing Principles: Learn essential rules for pipeline thinking, schema-first design, defensive parsing, idempotency, and fail-fast error handling.
• Ingestion Patterns: Guidance on handling various formats (CSV, JSON, Parquet, Excel, Database) and implementing incremental loading with schema validation.
• ETL/ELT Design: Understand layered architecture, error handling strategies, and orchestration basics for complex data workflows.
• Data Quality: Implement validation checks and data contracts to maintain data integrity.
• Analysis & Reporting: Best practices for summary statistics, output formats, and statistical reporting.
• Architecture Decisions: Insights into choosing between batch vs. streaming, storage solutions, and relevant tools.
• Use Case: You need to build a daily pipeline to ingest sales data from multiple sources, clean and transform it, and load it into a data warehouse for business intelligence reporting.

Quick Start

Follow the principles outlined in this guide to design a new data pipeline.