Books on data engineering, data management
2 min readMar 5, 2024
Few books, to refer for exceeding in Data related streams
Fundamentals of Data Engineering by Evanthia Dimara and Spiros Antonatos
- Provides a comprehensive overview of the foundational concepts and principles in data engineering.
- Covers essential topics such as data modeling, data pipelines, ETL processes, and data quality.
- Suitable for beginners looking to build a solid understanding of data engineering fundamentals.
Designing Data-Intensive Applications by Martin Kleppmann
- Explores the principles, techniques, and patterns used in building data-intensive applications.
- Discusses distributed systems, data storage, data processing, and data consistency models.
- Offers insights into designing robust and scalable data systems for modern applications.
Data Management at Scale by Piethein Strengholt
- Focuses on managing and scaling data infrastructure in large-scale systems.
- Addresses challenges related to data storage, processing, and management in distributed environments.
- Offers practical strategies and best practices for building scalable data platforms.
Spark: The Definitive Guide by Bill Chambers and Matei Zaharia
- Provides a comprehensive guide to Apache Spark, a widely used distributed computing framework for big data processing.
- Covers various Spark components, including Spark Core, Spark SQL, Spark Streaming, and MLlib.
- Offers practical examples, best practices, and performance optimization techniques for Spark applications.
Data Mesh by Zhamak Dehghani
- Introduces the concept of data mesh, a decentralized approach to managing and scaling data across organizations.
- Discusses the challenges of traditional centralized data architectures and proposes a new paradigm for organizing data teams and infrastructure.
- Offers insights into building data platforms that empower domain-oriented data teams.
The Data Warehouse Toolkit by Ralph Kimball and Margy Ross
- Provides a comprehensive guide to designing and building data warehouses for business intelligence and analytics.
- Covers dimensional modeling techniques, ETL processes, and best practices for data warehouse implementation.
- Offers practical advice and case studies for designing effective data warehouse solutions.
Streaming Systems by Tyler Akidau, Slava Chernyak, and Reuven Lax
- Focuses on building scalable and robust streaming data processing systems.
- Covers streaming architectures, event time processing, windowing, and fault tolerance.
- Offers insights into building real-time analytics and stream processing applications using frameworks like Apache Beam and Apache Flink.
These books collectively provide a solid foundation and advanced insights into various aspects of data engineering, data management, and real-time data processing.