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).
Fundamentals of Data Engineering: Plan and Build Robust Data Systems (Grayscale Indian Edition)
Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers..
Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems (Grayscale Indian Edition)
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, and more..
Data Engineering with AWS – Second Edition: Acquire the skills to design and build AWS-based data transformation pipelines like a pro
This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.
