March 29, 2024
Updated March 31, 2025
15 minute read
Exploring a Career as a Data Engineer
Data Engineering is a specialized field within technology focused on building and maintaining the systems that allow organizations to collect, store, process, and analyze large volumes of data. Think of Data Engineers as the architects and plumbers of the data world; they design the blueprints for data infrastructure and ensure the smooth flow of data through complex pipelines, making it ready for use by others like Data Scientists and Business Analysts.
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Find a path to becoming a Data Engineer. Learn more at:
OpenCourser.com/career/cjmsu1/data
Reading list
We've selected some 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 Engineer.
Provides a comprehensive overview of building serverless applications specifically on the AWS platform. It covers key AWS services like Lambda, API Gateway, and Kinesis, offering practical insights and real-world use cases. It's valuable for understanding the practical application of serverless principles within a major cloud ecosystem and is suitable for those looking to implement serverless solutions on AWS.
A recent publication focusing on leveraging AWS Lambda for building scalable and cost-effective serverless solutions. It covers basics to advanced deployment, including event-driven design, hyper-scaling, and operational techniques. is highly relevant for those specifically focused on AWS Lambda and seeking to deepen their expertise.
Provides a beginner-friendly introduction to data modeling, covering fundamental concepts, techniques, and diagramming. It includes hands-on exercises and self-tests to reinforce learning, making it suitable for high school and undergraduate students, as well as those new to the field.
This comprehensive guide provides a step-by-step approach to data transformation, covering topics such as data quality assessment, data cleaning, data integration, and data standardization. It is ideal for both business and IT professionals who need to understand and implement data transformation projects.
Offers a vendor-agnostic view of serverless computing, covering AWS, Azure, GCP, Kubernetes, and open-source options. It provides a broad understanding of the serverless landscape and helps in selecting appropriate technologies. It's a good resource for gaining a wider perspective beyond a single cloud provider.
The first volume in a series, this book offers a collection of universal data models applicable across various industries. It's a practical guide providing pre-built patterns for common business concepts like parties, products, and orders. This is an excellent reference for data modelers at all levels, particularly useful for jump-starting modeling projects.
Microservice architecture has become increasingly popular, and this book provides insightful patterns for implementing it effectively.
If you desire something that introduces web services and service-oriented architecture, this would be a good choice.
While not solely focused on data modeling, this book provides a comprehensive overview of the systems and concepts underlying modern data management. It discusses various data models in the context of distributed systems, scalability, and reliability, offering valuable insights for architects and engineers.
This handbook focuses on best practices and real-world applications of serverless architecture, particularly using the AWS Well-Architected Framework's Serverless Lens. It's designed for technology leaders and architects, offering insights into operational excellence, security, reliability, performance, cost optimization, and sustainability in a serverless context.
Offers a practical, step-by-step guide to relational database design, including data modeling principles. It's aimed at beginners and those without extensive technical backgrounds, making it suitable for high school or early undergraduate students and business professionals who need to understand database fundamentals.
Explores reusable data model patterns for common business structures. It helps in applying data modeling rules in an enterprise context and provides high-level models for various business areas. This valuable resource for experienced modelers and professionals looking for proven solutions to recurring modeling challenges.
While not solely focused on serverless, this book foundational text for understanding microservices architecture, which is highly relevant to serverless computing. It covers design, testing, deployment, and operational concerns of microservices. It is essential background reading for anyone designing complex serverless systems and is widely regarded as a key resource in the field.
This practical guide focuses on how to transform data for business intelligence applications. It covers data integration, data combining, and data harmonization. It valuable resource for data analysts and business intelligence professionals who need to transform data for reporting and analysis.
For developers moving to the next level with web services, this high fit for RESTful web services and APIs.
If your interest includes the use of BizTalk server, this valuable source of information.
The third book in the series delves deeper into universal data modeling patterns, offering more advanced and complex patterns. It's suitable for experienced data modelers looking to expand their pattern library and tackle more intricate modeling scenarios.
Focuses on the principles and practices for developing high-quality data models. It emphasizes the importance of data model quality and provides techniques for achieving it throughout the modeling process. It valuable resource for data modelers seeking to improve their craft and build robust, maintainable models.
Focuses on aligning data modeling with business needs and strategy. It emphasizes the importance of involving business stakeholders in the modeling process and provides techniques for creating high-level data models that have significant business impact. It's particularly relevant for business analysts and data professionals working closely with business teams.
An example-driven tutorial focusing on building event-driven serverless applications using AWS Lambda. It covers fundamental concepts, common patterns, and building larger applications by combining smaller examples. While published in 2016, it remains a useful resource for understanding the core mechanics of AWS Lambda and event-driven design, particularly for those new to the service.
Serverless architectures often leverage event-driven patterns. provides a deep dive into designing and implementing event-driven systems, which is highly relevant for building reactive and scalable serverless applications. It offers valuable architectural knowledge that complements serverless-specific technical guides.
Introduces agile methodologies for data warehousing and dimensional modeling, focusing on collaborative techniques like 'modelstorming'. It's relevant for teams adopting agile practices in their data projects and provides a practical approach to dimensional modeling.
Presents a user-driven approach to conceptual and logical data modeling, emphasizing collaboration with business stakeholders. It introduces techniques for building and refining data models based on user requirements. It valuable resource for both modelers and business analysts involved in data projects.
Serverless computing key aspect of cloud-native development. explores various patterns for building cloud-native applications, including concepts relevant to serverless, microservices, and distributed systems. It helps in understanding how serverless fits within the broader cloud-native landscape.
Targets developers looking to build full-stack applications using serverless technologies, specifically with React, AWS, and GraphQL. It provides a practical, hands-on approach to building modern applications leveraging serverless services. It's a good resource for those interested in the practical application of serverless in a full-stack context.
Delves into the theoretical and practical aspects of data modeling, including research on how practitioners approach the discipline. It's suitable for advanced students, researchers, and experienced professionals interested in the nuances and complexities of data modeling.
Provides a practical approach to building Function as a Service (FaaS) solutions using Azure Functions with C#. It good resource for developers specifically working with Azure Functions and the C# language.
Provides an overview of data warehousing and data mining techniques, including data mapping.
Given the mention of 'Serverless on Kubernetes' in the course list, this book provides essential knowledge on Kubernetes, a platform often used in conjunction with or as an underlying layer for serverless deployments. It covers deploying and managing containerized applications, which is relevant for understanding serverless options like Cloud Run or AWS Fargate.
Focuses on data modeling within the SAP HANA platform, covering techniques for building predictive, simulation, and optimization models. It's a specialized resource for professionals working with SAP HANA and provides practical guidance for advanced modeling scenarios in that environment.
Offers a comprehensive guide to serverless computing, including a self-assessment tool. It covers integrating recent advances and process design strategies according to best practices. It can serve as a reference for understanding various aspects of serverless computing and identifying areas for improvement.
DevOps principles are crucial for successfully implementing and operating serverless applications. This foundational book on DevOps provides valuable insights into creating a culture and practices that support rapid, reliable, and secure software delivery, which is highly applicable to serverless environments.
Discusses the shift to the 'Modern Cloud' and the principles behind it, with serverless being a significant component. It focuses on the socio-technical aspects and how organizations can leverage modern cloud technologies, including serverless, for business advantage. It's relevant for understanding the strategic importance of serverless.
Is one of the first books on data mapping and integration. It provides a broad overview of the field.
Provides a broad overview of Google Cloud Platform, including a section on serverless applications with Cloud Functions and managing containers with Kubernetes. While not exclusively serverless, it offers valuable context for those exploring serverless options within the Google Cloud ecosystem.
Delves into the foundations and applications of conceptual modeling, which is the initial stage of data modeling. It explores various conceptual modeling techniques and their use in capturing business requirements. It's a good resource for students and researchers interested in the theoretical underpinnings of data modeling. While not a recent publication, the foundational concepts remain relevant.
A book aimed at absolute beginners to serverless architecture. It introduces core concepts, benefits, and how to leverage serverless for efficient and scalable applications, focusing on best practices. This is an excellent starting point for anyone with no prior knowledge of serverless.
Provides a comprehensive overview of the field of spatial data analysis. It covers the different types of spatial data, the methods for analyzing spatial data, and the applications of spatial data analysis. It is written by two leading researchers in the field and is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of the field of mapping and spatial analysis. It covers the different types of maps, the methods for creating maps, and the applications of maps. It is written by a leading researcher in the field and is suitable for both undergraduate and graduate students.
An older publication focusing on the Microsoft Azure platform. While it may not cover the latest serverless services in detail, it provides foundational knowledge of building cloud-based applications on Azure, which can be helpful for understanding the evolution of serverless on this platform. It is more valuable as historical context than a current reference.
Provides a comprehensive overview of the field of crime mapping. It covers the different types of crime maps, the methods for creating crime maps, and the applications of crime maps. It is written by four leading researchers in the field and is suitable for both undergraduate and graduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/career/cjmsu1/data
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