May 1, 2024
Updated May 11, 2025
31 minute read
Data engineering is the backbone of the modern data-driven world, encompassing the design, construction, and maintenance of systems that collect, store, and process vast amounts of data. At its core, data engineering ensures that clean, reliable, and accessible data is available for analysis, enabling organizations to make informed decisions, optimize operations, and unlock new opportunities. For those with a curiosity for how data flows and a passion for building robust systems, a career in data engineering can be both intellectually stimulating and professionally rewarding. This field is dynamic, constantly evolving with new technologies and approaches, offering a continuous learning experience for those who embark on this path.
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Reading list
We've selected 34 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Engineering.
Provides a comprehensive overview of deep learning. It covers all aspects of deep learning, from the basics to the latest research.
Considered a modern classic, this book delves into the fundamental trade-offs and concepts behind building robust, scalable, and maintainable data systems. While not exclusively about data engineering, its in-depth coverage of distributed systems, databases, and data processing patterns is essential for any data professional looking to deepen their understanding. It is highly valuable for undergraduate and graduate students, as well as working professionals.
Another comprehensive guide to Apache Spark, co-authored by one of its creators. definitive resource for learning Spark for big data processing, covering its various components and APIs. It's a must-read for anyone serious about using Spark in their data engineering work, suitable for undergraduate, graduate, and professional levels.
Provides a comprehensive guide to using Apache Beam for building and managing data pipelines. It covers all aspects of Apache Beam, from installation and configuration to data ingestion and scheduling.
Provides a comprehensive overview of predictive analytics. It covers all aspects of predictive analytics, from data preparation to model building and evaluation.
Provides a comprehensive overview of big data. It covers all aspects of big data, from the challenges of big data to the opportunities that big data offers.
Provides a comprehensive overview of data visualization. It covers all aspects of data visualization, from the basics to the latest research.
Provides a practical guide to using Pandas for data analysis. It covers all aspects of Pandas, from data loading and cleaning to data manipulation and visualization.
Apache Spark widely used engine for big data processing. provides a comprehensive introduction to Spark, covering its core concepts and APIs. It's essential for anyone working with big data pipelines and aligns with courses mentioning Spark. The 3rd edition, published in 2020, covers recent features and improvements.
Aligning with the Google Cloud focused course titles, this book provides guidance on performing data engineering tasks on GCP. It's a valuable resource for those preparing for the Google Cloud Professional Data Engineer exam or working with GCP data services. It covers the relevant tools and services offered by Google Cloud.
Provides a deep dive into the concepts and challenges of building large-scale streaming data processing systems. It is essential for data engineers working with real-time data and stream processing frameworks. The book covers the theoretical foundations and practical considerations for designing robust streaming architectures.
Python fundamental language in data engineering. focuses on using Python for various data engineering tasks, including building data pipelines and working with large datasets. It is particularly useful for those whose background is in Python and want to apply their skills to data engineering, aligning with courses mentioning Python for Data Engineering. Published in 2020, it's a relatively recent resource.
Provides a practical guide to using data science for business. It covers all aspects of data science, from data collection to model building and deployment.
Dbt (data build tool) popular tool in the modern data stack for transforming data in the warehouse. focuses on using dbt for data engineering workflows, aligning with courses mentioning dbt. It's particularly relevant for professionals and graduate students working with cloud data warehouses and ELT processes.
Understanding how databases work internally is crucial for data engineers. provides a detailed look into the design and implementation of databases and distributed data systems. It's a highly technical book, best suited for graduate students and experienced professionals who want to deepen their understanding of the foundational technologies they work with.
Covers the fundamental principles and best practices for building scalable data systems in the big data era. It provides a broader perspective on designing data architectures that can handle large volumes of data and high traffic, valuable for undergraduates, graduates, and professionals involved in system design.
SQL foundational skill for data engineers. goes beyond basic SQL, covering advanced techniques for data analysis and transformation. It's a useful reference for anyone looking to deepen their SQL skills for data engineering tasks, relevant for all audience levels, especially those focusing on data manipulation and analysis. Published in 2021, it offers recent perspectives on SQL for data professionals.
Provides a comprehensive guide to machine learning with Python. It covers all aspects of machine learning, from data preprocessing to model building and evaluation.
Provides a practical guide to using data-driven marketing to improve marketing campaigns. It covers all aspects of data-driven marketing, from data collection to customer segmentation and targeting.
Provides a practical guide to building and managing data science teams. It covers topics such as hiring, training, and motivating data scientists, as well as best practices for data science project management.
Authored by the 'father of the data warehouse,' this book explores the concept of the data lakehouse, an evolution of data architecture combining the benefits of data lakes and data warehouses. It's a relevant read for understanding contemporary data architecture trends, particularly for experienced professionals and architects. Published in 2021, it's a recent contribution to the field.
Written by the creator of the pandas library, this book standard text for learning data manipulation and analysis using Python and its key libraries like pandas and NumPy. While not strictly a data engineering book, proficiency in these tools is essential for many data engineering tasks, making it a valuable prerequisite or supplementary read, particularly for high school and undergraduate students. The 3rd edition was published in 2022.
Offers a collection of insights and advice from various data engineering experts on a wide range of topics. It provides a broad perspective on different aspects of data engineering, from technical skills to best practices and industry trends. It's a valuable resource for gaining diverse viewpoints and understanding the multifaceted nature of the role.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/i8as52/data
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