Books on data engineering, data management

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.