Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems

Data engineering teams often meet familiar problems in unfamiliar pipelines: late-arriving records, duplicate events, fragile ingestion, unclear data lineage. Data Engineering Design Patterns gives those recurring challenges a practical vocabulary, organizing solutions as reusable patterns rather than one-off fixes.

Bartosz Konieczny takes readers from ingestion through observability, describing the problem each pattern addresses, its possible solutions, and the consequences to consider in real scenarios. The emphasis is on adaptable thinking: patterns are presented as technology-agnostic approaches, with open-source tools and public cloud services used in implementation examples.

Patterns for the whole data lifecycle

The book is arranged around the work of building and running data systems. Its chapters cover ingestion, error management, idempotency, data value, data flow, security, storage, data quality, and observability. This broad sequence helps connect individual engineering choices to the larger pipeline they serve.

From duplicate records to data lineage

Examples span incremental loading and change data capture, late-data handling and deduplication, aggregation and orchestration, access control and encryption, schema migration, and lineage tracking. The named patterns make it easier to compare approaches and revisit a solution when a similar problem appears in another project.

A practical reference for design decisions

Each pattern is presented in the context of a real engineering problem, including solutions and their trade-offs. That framing is useful when a team needs to reason not only about what could work, but also about the implications of choosing it. A concluding appendix gathers the patterns into a quick-reference summary.

Who may find it useful

Data engineers and data architects will find the most direct connection to their day-to-day work. The book may also be useful to practitioners responsible for batch or streaming systems who want a more systematic way to discuss reliability, data quality, security, and operational visibility.

For readers seeking reusable ways to think through pipeline challenges, this book offers a structured collection to consult as the work evolves.