1.Data Engineering Design Patterns – Bartosz Konieczny (Apr 2025)
Overview: A modern pattern-based guide for building robust data pipelines. Covers ingestion, quality, idempotency, observability, and implementations with Airflow, Spark, Flink, and Delta Lake.
2.Data Engineering Best Practices – Richard Schiller & David Larochelle (Oct 2024)
Overview: A comprehensive playbook covering cloud architecture, agile processes, pipeline design, cost/performance optimization, data security, and serverless microservices.
3.Fundamentals of Data Engineering – Joe Reis & Matt Housley (2022)
Overview: A thorough introduction to modern data engineering, including ETL, orchestration, modeling, warehousing, and cloud-native platforms like Beam, Spark, Kafka, AWS/GCP/Azure
4.Designing Data‑Intensive Applications – Martin Kleppmann (2017)
Overview: Seminal architecture book on storage, consistency, messaging, distributed systems, stream processing, and reliability.
5.Data Engineering with Python – Paul Crickard
Overview: A hands-on guide to building ETL workflows using Python, Airflow, Spark, Kafka, and cloud platforms (AWS/GCP).