Forem: Pavan Kalyan

Domain Modeling: Creating Conceptual Models for Software

Pavan Kalyan — Thu, 07 Sep 2023 06:33:46 +0000

Introduction

What is Domain Modeling?

Domain modeling refers to the process of creating a conceptual representation of a specific problem domain within a certain context. It is a fundamental step in software engineering.

A Domain Model provides a simplified view of the domain that helps stakeholders communicate and understand business needs and processes. It serves as the foundation for developing the software system.

Domain modeling also applies to front-end development. All User Interfaces, actions, interactions, and presentations have a strong basis in domain modeling.

Domain modeling involves identifying key entities, attributes, relationships, and constraints within the problem domain that the software aims to address. Tying together these four components are actions.

In Software Engineering, domain modeling plays a major role. Sometimes this is done implicitly, while other times it is called schema design or database design. The specifications come out of this process.

Essentially, Domain Modeling is representing the real world in software systems.

For example, Flipkart modeled the domain of selling books online. Uber modeled the domain of taxi services. Clear Tax modeled the domain of taxes.

How did people who built software start?

The first step in building software is typically domain modeling. However, with enough software engineering knowledge and programming experience, one could take a different approach.

Many people prefer jumping straight into coding. At some point though, you need to represent the business or product domain in the software due to the importance of understanding the domain.

Domain modeling features heavily in many big tech interviews.

The main components of a domain model are:

- Entities (Things, Nouns)

All entities behave in a way that is related to each other. Entities are the things that exist in the system. In Software Literature entities are called nouns. Ex: Take Twitter for example, If we observe on Twitter, a tweet is an entity, the user is an entity.

Obvious and Non-Obvious(Counter-Intuitive) entities: Some of the obvious entities that could exist in domain models for retail, media streaming, and library systems.

Retail:
- Obvious Entities: Customer, Product, Order, Invoice, Cart, Item.
- Non-Obvious Entities: Price Trend, Location.
Streaming Services:
- Obvious Entities: Movie, Song, Episode, Profile, Watch history, Genre.
- Non-Obvious Entities: Homepage, Most Popular, Recommendation, Viewing Session.
Library :
- Obvious Entities: Book, Author, Edition, Magazine, Shelf, Genre.

- Attributes (Properties)

Attributes are also entities or things but they don't have existence on their own.

Ex: Take Twitter for example,

A tweet is more than a text, it has a time stamp. So context time stamp bunch of other things make it a tweet. But the time stamp itself does not have any existence of its own. So it's an attribute of a tweet.
A user can have multiple attributes like names, locations, preferences, and pronouns.
Profile Picture could be an attribute or it could be an entity on its own.
When a user posts a tweet. Sometimes upload a picture to it and add text to the picture. So all the text is a property of the picture of all of the images the user uploads. So it is also an attribute of tweets.

Obvious and Non-Obvious(Counter-Intuitive) Attributes :

Obvious Attributes: Names, Emails, Birthdays.
Non-Obvious Attributes: Address, Location.

- Relationships (1 to N, N to N, 1 to 1)

These describe how the entities are associated or related to each other.

There are three types of relationships:

One-to-One (1:1) Relationship: This represents that an entity instance in one entity is related to a maximum of one instance in another entity 1:1 relationships allow unique identification of related entities.
Ex: A user has one profile, A Person has one SSN, A user has one timeline, and A user has one account.
One to Many(1:N) Relationship: This represents that an entity instance in one entity can be related to multiple instances in another entity. 1:N relationships allow the modeling of logical hierarchies.
Ex: A user makes many tweets ,A tweet has many likes, A user
follows many other users, A post has many comments, An author
writes many books.
Many to Many(M: N) Relationship: This represents that entity instances in each entity can be related to multiple instances in the other entity. M: N relationships require an associative entity to link them.

Ex: A tweet has been tagged with many hashtags, a hashtag is
associated with many tweets, a user is a member of many
groups, a student enrolls in many courses, and a supplier
provides many products.

Relationships have direction indicating traversal and access and Cardinality indicates how many instances can be related. Properly identifying and modeling relationships is crucial for an accurate domain model that reflects the real business or problem space being represented.

- Constraints (Rules, Restrictions)

Constraints in domain modeling define valid data values and business rules that apply to entities and relationships. Constraints are also known as Invariants. In the business world, it is also known as rules.

Famous Constraints :
- Username Uniqueness(Twitter/Facebook)
- 280 Character limit(Twitter)
- No name change on Facebook.
- Geofences on Uber.
- Return date must be after the start date. (Flight Tickets)
- Tweet must belong to a User (Referential)
- Video can only be 720p, 1080p, 1440p (Domain)
- Unique Season Number and Episode Number (Key) Book must have a Name (Null)
- Latitude must be between [-90, 90]
- Not So Obvious Constraints :
- No Sign Up Until you read the Terms and conditions.
- Only regular voters count in the IMDb Top 250.
- Can only unpause Subscription X days after Pausing.
- Must Wait Y months to delete account on .
- No “Back” button on Banking Websites.

Some constraints are enforced by logic, data types or databases.

- Actions (Verbs)

Actions are exactly what you would expect and what you would intuitively figure out of it. The key is to identify the domain actions. Actions represent the behaviors, functions or operations that can be performed by the entities in the domain model. Actions are central to software engineering. Actions are what you typically build in software.

Conclusion

Domain modeling is not exclusive to backend development; it plays a crucial role in frontend development too. By embracing domain modeling principles and techniques, front-end developers can create user-friendly, efficient, and maintainable interfaces that align with the problem domain. Whether you're building a personal project or contributing to a larger team, mastering domain modeling in the context of front-end development will undoubtedly enhance your skills and make you a more effective developer. Happy coding!

"Comparing Netlify, GitHub Pages and AWS: A Detailed Look at Three Popular Static Website Hosting Platforms"

Pavan Kalyan — Sat, 07 Jan 2023 22:00:27 +0000

When it comes to hosting static websites, there are a plethora of options available to developers. Two of the most popular platforms are Netlify and GitHub Pages, but in this article, we will also take a detailed look at hosting static websites on AWS.
In this article, we will compare Netlify and GitHub Pages to help you understand the pros and cons of each platform and choose the one that is best for your needs.

Netlify is a platform-as-a-service (PaaS) provider that offers a simple, yet powerful, hosting solution for static websites. With Netlify, developers can easily deploy and manage their websites directly from their Git repositories. The platform also provides a wide range of features such as continuous deployment, automatic SSL, and custom domains. Additionally, Netlify offers a free plan with basic features and a generous free tier, making it a great option for those on a tight budget.

GitHub Pages, on the other hand, is a free hosting service offered by GitHub. With GitHub Pages, developers can easily host their static websites directly from their GitHub repositories. The platform also offers automatic SSL, custom domains, and built-in integration with other GitHub features such as pull requests and issues. However, unlike Netlify, GitHub Pages only supports Jekyll, a static site generator, and does not offer as many features as Netlify.

When it comes to AWS, it provides a more comprehensive and flexible solution for hosting static websites. AWS offers a wide range of services that can be used to host static websites, including Amazon S3, Amazon CloudFront, and AWS Elastic Beanstalk. These services provide a high level of scalability, security, and performance. Additionally, AWS provides a free tier for the majority of its services, making it a cost-effective option for those on a budget.

While Netlify and GitHub Pages are both great options for hosting static websites, AWS offers a more comprehensive and flexible solution for hosting static websites. With AWS, developers can take advantage of a wide range of services to build and deploy their websites, including Amazon S3, Amazon CloudFront, and AWS Elastic Beanstalk. Additionally, AWS provides a free tier for the majority of its services, making it a cost-effective option for those on a budget.

In conclusion, while both Netlify and GitHub Pages are great options for hosting static websites, AWS provides a more comprehensive and flexible solution for hosting static websites. With a wide range of services and a free tier, AWS is a great option for developers looking for a cost-effective and scalable hosting solution for their static websites.

The Ultimate Guide to Learning Python: Resources, Practice, and More

Pavan Kalyan — Tue, 23 Mar 2021 18:36:19 +0000

Are you looking to learn Python, one of the most popular programming languages in the world? With its versatility and wide range of applications, Python is a great language to add to your skillset. Whether you want to build web applications, work in data science, or automate tasks, Python has something to offer. In this guide, we'll provide a list of resources and tips to help you get started with Python and become a proficient programmer.

First things first - make sure you are using the latest version of Python, which is currently Python 3. You can find out more about the different Python versions and their end of life dates on the Python developer guide.

https://devguide.python.org/devcycle/#end-of-life-branches

To get started with Python, you can check out tutorials such as "Automate the Boring Stuff with Python" or the tutorial provided by MDN. These guides are great for learning the basics of Python and getting a feel for the language. If you want to delve into more advanced topics, you can check out TutorialsPoint's Python tutorial or the PyCon 2017 tutorial on regular expressions.

☯ Automate the Boring Stuff with Python, very popular and easy guide to Python.

https://automatetheboringstuff.com/

☯ Python by MDN, a tutorial that is being written rn by MDN, most parts are done already.

https://developer.mozilla.org/en-US/docs/Learn/Drafts/Python

TutorialsPoint's Python that covers some advanced topics like GUI programming, Database access and so on.

https://www.tutorialspoint.com/python3/

☯ Regular Expressions PyCon 2017 it seems a little bit confusing at first but believe me, the job it is doing for you is a lot more complex! (common interview question too!) you can test your patterns using regex101.

Regular Expressions PyCon 2017:

https://www.youtube.com/watch?v=abrcJ9MpF60

regex101: https://regex101.com/

Once you've learned the basics, it's important to practice what you've learned to improve your skills and cement your knowledge. Sites such as Practice Python and Codewars offer exercises and challenges to help you put your Python skills to the test.

Practice

It's a good idea to do some exercises after learning the basics. Practice Python has some challenging ones with solutions.
Codewars is where developers achieve code mastery through a challenge.

Practice Python:

http://www.practicepython.org/

Codewars:

https://www.codewars.com/

If you're interested in web development, Python has a number of powerful frameworks to choose from, including Django. Django is a high-level web framework that makes it easy to build and deploy web applications quickly and securely. You can learn more about Django through tutorials such as the one provided by MDN or the official Django documentation.

Full Stack
In case you want to learn web development, you got Django!, it's high-level web framework for fast development, it's as easy and secure as any framework can be, some good tutorial for learning Django:

Django by MDN. MDN got a very nice tutorial to start learning Django.

https://developer.mozilla.org/en-US/docs/Learn/Server-side/Django

Official Django Documentation is a good start too.

https://docs.djangoproject.com/en/1.11/

Small Open-Source Django Projects you always learn from others' projects.

https://simpleisbetterthancomplex.com/2015/11/23/small-open-source-django-projects-to-get-started.html

For those interested in data science, Python has a number of tools and libraries to offer. The Anaconda Python distribution is a great resource for data scientists, as it includes a range of packages and tools for data analysis and visualization. You can also practice your data science skills through YouTube playlists and Kaggle competitions.

Data Science

Anaconda a Python distribution with all you need for data science.

https://www.anaconda.com/downloads

Youtube playlists about graphics and basics on machine learning.

https://www.youtube.com/user/sentdex/playlists

Kaggle Competitions, practice the theory you've learned in real-world scenarios.

https://www.kaggle.com/competitions

To stay up to date on the latest Python news and developments, you can check out PyVideo, Python Weekly, and the official Python docs. You can also search for and collaborate on open source Python projects on Github, or check out curated lists of Python resources such as "awesome-python" or "f**king-awesome-python."

With the right resources and practice, you can become a proficient Python programmer in no time. So start learning and see what Python can do for you!

Keeping you updated

PyVideo contains Python Conferences about almost everything.

http://pyvideo.org/

Python Weekly A weekly newsletter featuring curated news, articles, new releases, jobs etc related to Python.

https://www.pythonweekly.com/

Github search, find and collaborate on GPL Projects written in python.

https://github.com/search?utf8=%E2%9C%93&q=language:Python+license:gpl&type=Repositories&ref=advsearch&l=Python&l=

Reference

Official Python Docs.

https://docs.python.org/3/

awesome-python a curated list of awesome Python frameworks, libraries, software and resources.

https://github.com/vinta/awesome-python

f**king-awesome-python a curated list of Github stars and forks stats based on awesome awesome-python.

https://github.com/trananhkma/fucking-awesome-python

Thank you for reading this guide on learning Python. I hope that you found it helpful and that it has given you some useful resources and tips to get started with Python. Whether you are a beginner looking to learn the basics or an experienced programmer looking to expand your skillset, we hope that this guide has provided you with the information you need to succeed. Thank you for considering Python as your next programming language and we wish you the best of luck on your journey to becoming a proficient Python programmer.