May 1, 2024
Updated June 18, 2025
19 minute read
Data persistence, at its core, refers to the characteristic of data that allows it to outlive the process that created it. In simpler terms, it's about making sure your data sticks around, even after the application that generated or used it has closed, the computer has been turned off, or a system has been rebooted. This is a fundamental concept in computing, ensuring that information is not lost and can be reliably accessed and modified over time. Without data persistence, every time you closed a program, all the information within it would vanish, making modern computing as we know it impossible.
Working with data persistence can be quite engaging. Imagine designing systems that securely store and retrieve vast amounts of critical information for industries like finance or healthcare – the responsibility and the intellectual challenge are immense. Furthermore, the field is constantly evolving with the rise of cloud computing and Big Data, presenting continuous learning opportunities and the chance to work with cutting-edge technologies. The ability to ensure data integrity and availability directly impacts business operations and innovation, making roles in this domain crucial and often exciting.
What Exactly is Data Persistence?
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Reading list
We've selected 33 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 Persistence.
Provides a comprehensive overview of the fundamental concepts and challenges in designing data systems. It covers various data storage and processing technologies, making it excellent for gaining a broad understanding of data persistence in modern applications. It is highly regarded in the industry and is valuable as both a learning resource and a reference.
Explores the inner workings of various data systems, including databases and distributed systems. It's excellent for deepening one's understanding of how data persistence is implemented at a lower level and in distributed environments. It valuable resource for those interested in the mechanics of data storage and retrieval.
Introduces the fundamental concepts for designing, using, and implementing database systems. It covers database modeling, design, and implementation techniques, providing a solid foundation for understanding data persistence. It is suitable as a textbook for introductory to advanced database courses.
Focuses specifically on Entity Framework Core, a popular ORM for .NET development. It's highly relevant for developers working with ASP.NET Core and C#, covering practical aspects of data persistence in that ecosystem. It useful reference for developers using EF Core.
Provides a practical and easy-to-understand guide to relational database design. It's an excellent resource for beginners to grasp the fundamentals of structuring data for persistence in a relational model. It's highly recommended for those new to database design.
Provides a concise introduction to the concepts behind NoSQL databases and when to use them. It's valuable for understanding contemporary approaches to data persistence beyond traditional relational databases. It serves as a good starting point for exploring the diverse landscape of NoSQL options.
Covers using Redis, an in-memory data structure store, for data persistence. It teaches the basics of Redis and how to use it in application development.
Offers an in-depth look at Core Data, the primary persistence framework for iOS and macOS. It's essential for developers needing to deeply understand and utilize Core Data for their applications. It specialized book for Apple platform developers.
Delves into the theoretical underpinnings of the relational model and SQL. It's excellent for deepening one's understanding of relational databases, which are central to much of data persistence. It is more theoretical and best suited for those who want a rigorous understanding.
Definitive guide to dimensional modeling, a technique widely used in data warehousing and business intelligence. It provides a deep dive into designing databases for analytical purposes, offering a different perspective on data persistence focused on reporting and analysis.
Provides a comprehensive guide to MongoDB, a popular NoSQL document database. It's valuable for understanding data persistence in a NoSQL context and working with document-oriented data. It serves as a complete reference for MongoDB users.
Focuses on Apache Cassandra, a distributed NoSQL database. It's relevant for those interested in data persistence in highly scalable and distributed environments. It provides a deep understanding of Cassandra's architecture and usage.
While not solely focused on data persistence, this classic book covers architectural patterns for enterprise applications, including patterns for data access and persistence. It provides valuable context for how data persistence fits into larger application designs. It foundational text for software architecture.
Covers data warehousing, a specific type of data persistence used for business intelligence and data analysis. It teaches the basics of data warehousing and how to design and implement a data warehouse.
Deep dive into optimizing and managing MySQL, a widely used relational database. It's valuable for understanding performance considerations and operational aspects of data persistence in a production environment using MySQL.
Apache Spark powerful processing engine, and this book covers how it interacts with various data sources for persistence and processing. It's relevant for understanding data persistence in the context of big data processing. It key resource for Spark users.
While primarily a messaging system, Kafka is often used for durable storage of data streams, making it relevant to data persistence in big data architectures. provides a comprehensive guide to Kafka. It's valuable for understanding stream processing and data pipelines.
This collection of research papers provides insights into the history and evolution of database systems. It's valuable for those seeking a deeper, more academic understanding of the foundational ideas and significant advancements in data persistence research. It's more suitable for graduate-level students and researchers.
Covers designing and building data-intensive applications. It discusses various types of data persistence, including file-based, database, and cloud-based, and how to choose the right type for an application.
Discusses fundamental principles of software architecture and design, which are relevant to designing maintainable and testable data persistence layers. It provides valuable insights into structuring code that interacts with databases and other persistence mechanisms. It widely recommended book for software developers.
This book, while a general Android programming guide, includes sections on data persistence using SQLite and other Android-specific mechanisms. It's valuable for those focusing on mobile data persistence on the Android platform. It serves as a practical guide with hands-on examples.
Similar to its Android counterpart, this book covers iOS development and includes sections on data persistence using Core Data and other iOS-specific methods. It's a practical resource for developers interested in data persistence on the iOS platform.
This influential book on software design discusses organizing business logic around domain models, which impacts how data is structured and persisted. It provides valuable principles for designing the domain layer that interacts with data persistence. It foundational book for complex software development.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/g7028w/data
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