dev-data

Build a reliable data pipeline by applying schema-first design, defensive parsing, and idempotency. This Skill guides ingestion patterns for CSV, JSON, and Parquet, ensuring data integrity through fail-fast error handling and validation checks.

Implement incremental loads in ETL workflows using schema validation and defensive parsing. This Skill provides ingestion patterns to process only new or changed data, reducing overhead while maintaining data quality and integrity across pipeline runs.

Ensure data quality when ingesting sales data by enforcing data contracts and validation checks. This Skill outlines defensive parsing and fail-fast error handling strategies to catch anomalies early and maintain data integrity during transformation.

Choose batch processing vs streaming based on your architectural decisions and latency requirements. This Skill provides insights into selecting between batch and streaming architectures, guiding you to the appropriate storage solutions and orchestration tools.

Schema-first design prevents data pipeline failures by validating formats like JSON and Parquet before processing. This Skill explains how defensive parsing and idempotency principles create robust ETL workflows that handle schema variations gracefully.

Yes, you can use this guide for ELT workflows and database ingestion. It covers layered architecture, ingestion patterns for databases, and orchestration basics, helping you design complex data workflows with proper error handling.