FastAPI with SQLAlchemy: Building Robust APIs with Databases

In my latest tutorial, I explored the transition from TinyDB to SQLite when building Python web applications. While TinyDB is great for beginners because it stores data in simple JSON files and is easy to set up, it has limitations as applications grow. Querying large datasets, managing structured data, and handling multiple users can become challenging. That’s why I introduced SQLite as a practical alternative. SQLite provides a lightweight relational database with powerful SQL queries, better data integrity, and improved performance—all without requiring a separate database server. For developers building apps with Python and Flask, SQLite is often the next logical step when moving from simple prototypes to more structured applications. Here is the tutorial link: https://lnkd.in/g8UYe2pz #Python #Flask #SQLite #WebDevelopment #Programming #Database

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

Every project has conventions. Folder structures, error formats, database rules that took three bug postmortems to establish. The problem? You end up re-explaining them to Claude in every single conversation. Claude Code Skills fix that. You write your conventions once — as a plain Markdown file — and Claude follows them automatically, every time. I wrote a practical breakdown of how this works on a Flutter + Python + PostgreSQL stack: three Skills that cover widget conventions, FastAPI patterns, and PostgreSQL migration rules. If you're tired of pasting the same context into every prompt, this one's for you. 👇

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.

Revisiting Django ORM I reviewed some advanced ORM concepts in Django, and I’m starting to appreciate how powerful it really is when used properly. A few concepts that stood out: Atomic transactions – ensure operations either fully succeed or fully fail. Critical for financial logic and data integrity. F expressions – perform database-level field operations without pulling data into Python. Safer and more efficient. Q objects – build complex queries with AND/OR logic cleanly. Case & When – conditional logic directly in SQL via the ORM. Powerful for annotations and computed fields. Subquery, OuterRef & Exists – write correlated subqueries and advanced filters without raw SQL. Generated fields & annotations – compute values at query time instead of storing redundant data. Multiple databases – route reads/writes strategically when scaling. Using logic closer to the database makes your application faster and more efficient. #Django #BackendEngineering #Python #ORM

Low code using DataPrep #machinelearning #datascience #lowcode #dataprep DataPrep lets you prepare your data using a single library with a few lines of code. DataPrep.EDA DataPrep.EDA is the fastest and the easiest EDA tool in Python. It allows data scientists to understand a Pandas/Dask DataFrame with a few lines of code in seconds. DataPrep.Clean DataPrep.Clean aims to provide a large number of functions with a unified interface for cleaning and standardizing data of various semantic types in a Pandas or Dask DataFrame. DataPrep.Connector DataPrep.Connector provides an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow. Streamline calls to multiple APIs through one intuitive library. DataPrep.Connector also support loading data from databases through SQL queries. With one line of code, you can speed up pandas.read_sql by 10X with 3X less memory usage! https://lnkd.in/gjJMXHbn

🆕 My latest blog is out which covers AWS Lambda Powertools for Python. It's the library I add to every Lambda function before writing any business logic, and I wanted to put everything I've learned from using it across real projects into one comprehensive guide. The post covers the core observability stack: Logger, Tracer, and Metrics - along with utilities like idempotency, batch processing, event routing, and parameters. I included Terraform and SAM examples from two of my own projects to keep things practical. If you're building with Lambda and spending more time on observability boilerplate than business logic, this might save you some effort. Three decorators can replace a surprising amount of code. Check it out! https://lnkd.in/evB8U4np

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

🚀 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