Django ORM Queries & CRUD Operations with Python

🚀 Backend Journey: Day 4 - Writing Python, Generating Tables! 📌 Topic: The Power of Django Models and the ORM Yesterday, I broke down the MVT architecture. Today, I zoomed in on the "M" — the Model. In the past, whenever I thought about interacting with databases, I pictured writing long, complex SQL queries to create tables, insert records, and fetch data. Django handles this completely differently through its ORM (Object-Relational Mapping). Here is what I learned today about how Django manages data: 🔹 Classes over Queries: Instead of writing raw SQL like CREATE TABLE users..., I simply define a Python class. Each attribute in the class (like name = models.CharField(...)) automatically translates into a database column. It feels incredibly intuitive. 🔹 Database Agnostic: Because I'm writing Python, Django translates that code into the correct SQL for any database. I can develop locally using lightweight SQLite and switch to a robust PostgreSQL database for production by changing just one line in my settings. No need to rewrite any queries! 🔹 Built-in Security: By using the ORM to interact with the database, Django automatically parameterizes queries behind the scenes. This provides massive built-in protection against SQL Injection attacks. 🧠 Key Insight: The ORM bridges the gap between object-oriented programming and relational databases. It allows me to interact with my data purely as Python objects, which speeds up development tremendously and keeps the codebase clean and secure. Next up: I'll be exploring Migrations—how Django keeps track of all these database changes like a version control system for your database! 👇 Question for the experienced devs: Do you prefer the speed and safety of an ORM, or do you still drop down to raw SQL when dealing with highly complex queries? #Python #Django #DatabaseDesign #ORM #BackendDeveloper #SQL #SoftwareEngineering #LearningInPublic #WebDevelopment

Mastering Django & `django-admin-charts`: Data Visualization In the world of web development, data is king. Being able to effectively visualize and interpret data is crucial for understanding trends, making informed decisions, and communicating insights to others. In Django, the powerful Python web framework, the `django-admin` interface provides a solid foundation for managing your data. However, the standard admin interface lacks built-in charting capabilities. This is where `django-admin-charts` comes in....

Writing a Django query does not mean it hits the database! Writing a Django query and hitting the database are two different things. The ORM makes it easy to forget the difference between these. Here's what actually happens: 1. When a QuerySet is written, Django builds a query object internally. 2. This has no SQL. No database connection. Just a description of what data is needed. 3. This description can be chained, filtered, sliced, and passed around as required. Still no database hit. 4. The database is only contacted at the moment of evaluation, when Python actually needs the data. This is powerful when used intentionally. Build a complex query across multiple functions, evaluate once, pay the DB cost once! This can backfire too! A QuerySet evaluated inside a loop, once per iteration, is the N+1 problem. Takeaway: -> Efficiency: Chain and pass QuerySets, zero DB cost until evaluation -> Danger: Evaluation inside loops fires one query per row silently -> Control: Knowing evaluation points is the foundation of ORM performance I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

🚀 Day 69 – Database Design & Django Models Continuing the development of my project in the 90 Days of Python Full Stack journey, today I focused on designing the database structure and implementing the core Django models. 📌 Project: Real-Time Emergency Alert & Live Location Sharing System A well-structured database is the backbone of any application. Before implementing advanced features, it is important to define how data will be organized, stored, and related within the system. 🔹 Work completed today • Designed the database structure for the application • Created Django models to represent system entities • Defined relationships between users, alerts, and emergency contacts • Implemented fields for storing location coordinates and timestamps • Prepared the models for database migrations 🔹 Core Models in the System • User Model – Stores user information and authentication details • Emergency Contact Model – Stores guardian or parent contact information • Alert Model – Records emergency alerts triggered by users • Location Model – Stores latitude, longitude, and alert-related location data Designing these models ensures that the system can efficiently handle user alerts, location tracking, and emergency notifications. 📌 Day 69 completed — the database structure for the emergency alert system is now defined. #90DaysOfPython #PythonFullStack #Django #DatabaseDesign #BackendDevelopment #ProjectDevelopment #LearningInPublic

Today I learned about Models in Django and finally understood the concept of ORM. ORM stands for Object Relational Mapper. It means we can create and manage database tables using Python instead of writing SQL directly. In Django, we define tables inside the models.py file. Each model class represents a table, and each attribute inside the class represents a column. What I understood: We write everything in Python. Django automatically converts it into database queries. No need to manually create tables in SQL. 🔹 Some Common Fields I Learned: CharField() – used to store text (like name, title, etc.) TextField() – used for long text IntegerField() – used to store numbers FloatField() – used for decimal values BooleanField() – used for True/False DateField() – used for dates EmailField() – used to store email addresses After creating models, we run migrations so Django can create the tables in the database. Today’s learning helped me understand how Django connects Python code with the database smoothly. Slowly everything is connecting — Views, Templates, and now Models. #Django #Python #WebDevelopment #BackendDevelopment #LearningJourney

The Django ORM feels like magic. But how does it work internally? It's actually three layers doing very specific jobs. Layer 1: Query -> When a QuerySet is built, Django creates a Query object internally. -> This object is a structured, database-agnostic representation of the intended operation. -> All this is stored as Python data structures. No SQL yet. This is why QuerySets are chainable. Layer 2: SQL Compiler -> At evaluation time, Django takes the Query object and hands it to a SQLCompiler. -> Compiler translates Python representation into actual SQL. -> This is where database-specific syntax is handled. The same Query object produces different SQL depending on which backend is active. Layer 3: Connection -> The compiled SQL string is handed to the database Connection. -> This manages the actual connection to the database, sends the query, and returns raw results. -> Django then maps those raw results back into model instances. The ORM is not a black box. It's a pipeline and knowing it means knowing exactly where to look when something goes wrong. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

When learning backend development with Django, many developers understand models and queries but struggle with one key concept: How APIs are actually created. In Django, views are where everything comes together. Queries retrieve the data. Serializers convert the data. But views expose the data as APIs. In my latest article, I break down: • What Django views are • Function-based vs class-based views • What API views are • How APIs return JSON responses • Best practices for building clean API endpoints If you're learning backend development or building APIs with Django REST Framework, this will help you understand how requests actually become APIs. Read the article here 👇 https://lnkd.in/dquzq8bZ #Django #Python #BackendDevelopment #APIs #SoftwareEngineering #WebDevelopment

Our first integration, Django, is in the Django Newsletter this week. ParadeDB is now officially available from the Python / Django ecosystem via `pip install django-paradedb` Source: https://lnkd.in/g7gyYsD5

Build search and RAG apps with boring technology. Django + Postgres. Most times technology that you already know is the best.

Wait… Django can work with databases without writing SQL? While learning Django for backend development, I came across ORM (Object Relational Mapping). Until then, I thought working with databases always meant writing SQL queries like `SELECT`, `INSERT`, `UPDATE`, and `DELETE`. But Django introduced a different approach. With ORM, database tables become Python classes, and rows become objects. Instead of writing SQL manually, you work with Python code, and Django handles the queries behind the scenes. Another interesting part was flexibility in many cases, you can switch databases (like SQLite to PostgreSQL or MySQL) just by changing the configuration. Coming from Flutter, where I used sqflite and wrote raw SQL queries, this felt like a completely different way of working. While tools like Drift or Floor offer ORM-like features, direct SQL is still common. It’s a simple concept, but it changed how I think about databases in backend development. Still learning, still exploring. #Django #Python #ORM #BackendDevelopment #SoftwareDevelopment #LearningJourney #BufferBytesTechnologies