Covers data ingestion, quality, and idempotency for end-to-end data engineering projects
Includes user-facing problem descriptions and real-world consequences for each pattern
Uses open source data tools and public cloud services for practical application
Technology-agnostic approach to solving common data engineering challenges
Guides both beginners and experienced professionals in building scalable data systems
Summarized by Shop
Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency,