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How do you adapt your object oriented design to different data sources and formats?

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Object oriented design (OOD) is a powerful technique for creating modular, reusable, and maintainable software. However, when you work with different data sources and formats, such as relational databases, JSON files, or web APIs, you may face some challenges in applying OOD principles. How do you adapt your OOD to different data sources and formats? In this article, you will learn some tips and best practices to help you design flexible and robust data models, interfaces, and adapters using OOD.

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1 Data models

The first step in adapting your OOD to different data sources and formats is to define your data models. Data models are classes that represent the entities and relationships in your domain, such as customers, products, or orders. Data models should encapsulate the data attributes and behaviors of these entities, and provide methods for validating, manipulating, and accessing them. Data models should be independent of the data sources and formats, and follow the OOD principles of abstraction, encapsulation, inheritance, and polymorphism.

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2 Data interfaces

The second step in adapting your OOD to different data sources and formats is to define your data interfaces. Data interfaces are abstract classes or interfaces that specify the contracts or expectations for interacting with the data sources and formats. Data interfaces should define the methods for creating, reading, updating, and deleting (CRUD) data models, as well as any other operations that are relevant to your domain, such as searching, filtering, or aggregating. Data interfaces should be consistent and coherent across different data sources and formats, and follow the OOD principle of interface segregation.

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3 Data adapters

The third step in adapting your OOD to different data sources and formats is to implement your data adapters. Data adapters are concrete classes that implement the data interfaces for specific data sources and formats. Data adapters should handle the details of connecting, querying, parsing, and transforming the data from and to the data models, using the appropriate libraries or tools for each data source and format. Data adapters should be interchangeable and adaptable, and follow the OOD principles of dependency inversion and Liskov substitution.

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Jasmine K.

Software Engineer / Analyst | BSc (Hons) in Pure Mathematics
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The dependency inversion principal is just one of the SOLID design principles, which are all worth bearing in mind for improved robustness and stability. In this case, for the dependency inversion principle for example, high level modules should be easily reusable and should depend on abstractions and not on low level modules (de-couple the high and low level modules). Both high and low level modules then depend on the abstraction in fact, so the name dependency inversion is a bit misleading. The Liskov Substitution Principle (also part of SOLID) was introduced by Barbara Liskov in 1987. In simple terms, the principle states that objects of a superclass should be replaceable by objects of its subclass without breaking anything.

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4 Data factories

The fourth step in adapting your OOD to different data sources and formats is to use data factories. Data factories are classes or functions that create and return the appropriate data adapters based on some criteria, such as configuration settings, user input, or runtime conditions. Data factories should abstract away the complexity of choosing and instantiating the correct data adapters, and provide a single point of access for the rest of the application. Data factories should be simple and reliable, and follow the OOD principle of single responsibility.

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Jasmine K.

Software Engineer / Analyst | BSc (Hons) in Pure Mathematics
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Data factories are a design pattern used to encapsulate the creation and instantiation of complex data structures or objects. Centralising this creation and instantiation offers many benefits such as - making it easier to maintain and modify the construction process - promoting re usability of the same creation logic in other parts of the program - abstracting away implementation details of object creation for flexibility (decoupling) - facilitating testing of different configurations of objects and data

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5 Data strategies

The fifth step in adapting your OOD to different data sources and formats is to use data strategies. Data strategies are classes or functions that define the logic or rules for selecting, combining, or switching between different data sources and formats, depending on the needs or preferences of the application or the user. Data strategies should encapsulate the knowledge and reasoning about the data sources and formats, and provide a consistent and transparent way of working with them. Data strategies should be flexible and extensible, and follow the OOD principle of open-closed.

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6 Data examples

The final step in adapting your OOD to different data sources and formats is to provide some data examples. Data examples are code snippets or diagrams that illustrate how to use the data models, interfaces, adapters, factories, and strategies in practice. Data examples should demonstrate the benefits and challenges of using OOD for different data sources and formats, and show how to apply the OOD principles and best practices in real scenarios. Data examples should be clear and concise, and follow the OOD principle of don't repeat yourself.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Paulo Zemek

Software Engineer - I used talk to computers and they understood me even without AI, we just spoke the same language... now that they got smarter we are so in-sync that sometimes it feels we talk to each-other mentally!
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The article talks about "data examples" and I feel that's exactly what it's lacking. Examples. Could you present a situation where we have different data sources and how different they are? How to use the data adapters? In fact, the entire article seems to present what I call an anti-pattern. The same things, that could just be saved differently by having just the appropriate converters registered once, is actually split into 3 or more classes that represent exactly the same, each one which a slight difference, and copy/modifications need to be done by hand at every use of a property. I presented the problem in the article: https://www.codeproject.com/Articles/5338856/Patterns-and-Frameworks-Whats-Wrong

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Jasmine K.

Software Engineer / Analyst | BSc (Hons) in Pure Mathematics
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This article is very general. I started to contribute by trying to add more detail but in actuality the whole thing is just so generic and has gaping holes and lots of words without really explaining things practically. It needs real world examples for sure! It also needs to actually answer the question, which it doesn’t. It feels repetitive, vague and incomplete. To be fair, even if you have best practices and design principles, in the end, you will have to get specific depending on your domain and the problem you are having to solve, and you can’t just blanket apply text book design principles.

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Louis Langholtz

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I like Jasmine’s comments on this. Additionally, I have a disconnected sense about the article between its title and implied goals. For instance, I don’t find OOP necessarily “powerful”. It’s a technique, agreed. But things like power, are more in the eyes of the beholder.

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How do you adapt your object oriented design to different data sources and formats?

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1 Data models

2 Data interfaces

3 Data adapters

4 Data factories

5 Data strategies

6 Data examples

7 Here’s what else to consider

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