As I progress through Phase 3 of my software engineering journey, the shift toward backend development has been both challenging and rewarding. This phase introduces us to the foundational technologies and practices that power real-world applications — from databases and ORMs to version control for schema changes and efficient data handling.
In this blog, I’ll explore some key technologies and best practices we’ve covered in Phase 3, and how they align with industry standards.
1. Python: The Foundation of Modern Backend Systems
Python continues to dominate backend development due to its readability, rich ecosystem, and flexibility. In Phase 3, we moved beyond basic syntax into more complex backend structures such as:
Object-Oriented Programming (OOP)
Data modeling with classes
Using built-in data structures effectively (lists, dictionaries, sets, and tuples)
Best Practice: Embrace OOP design patterns like composition over inheritance to keep your models maintainable and extensible.
2. SQLite: Lightweight but Powerful
SQLite has been our introduction to relational databases. It’s simple, serverless, and perfect for development environments.
We learned how to:
Design normalized schemas
Write CRUD SQL queries
Enforce data integrity with constraints
Industry Insight: SQLite is widely used in embedded systems and mobile apps. However, in production environments, teams often use PostgreSQL or MySQL for scalability.
3. SQLAlchemy: The ORM That Bridges Python and SQL
SQLAlchemy offers a high-level abstraction over SQL while preserving full control. We explored both the Core and ORM layers.
Some key features we practiced:
Mapping classes to tables using declarative base
Creating relationships using ForeignKey and relationship()
Executing queries using the session object
Best Practice: Use SQLAlchemy ORM for readability and rapid development, but understand Core for performance-critical operations.
4. Alembic: Managing Database Migrations Like a Pro
Alembic is the go-to tool for version-controlling database schema changes. It's used alongside SQLAlchemy to track migrations.
In practice, we:
Initialized Alembic in our projects
Generated migration scripts automatically
Applied schema changes in a controlled, reversible way
Industry Insight: Alembic is critical for teams to manage evolving data models in collaborative environments. Think of it as Git for your database schema.
5. Data Structures: Organizing Information Efficiently
Beyond backend tools, understanding core data structures is essential for building performant systems.
Key concepts we applied:
Lists and tuples for ordered data
Sets for uniqueness and quick membership tests
Dictionaries for fast key-value access
Best Practice: Choose the right data structure based on access time, mutability, and memory efficiency. For example, prefer sets when membership checks dominate.
Final Thoughts
Phase 3 has been a crucial step in transitioning from frontend logic to full-stack development. Understanding how backend components interact — and managing data responsibly — is the backbone of any scalable software project.
As I continue building backend services, these tools and practices are not just academic exercises — they are industry-standard skills that will shape my future as a developer.
Stay tuned for my next post, where I’ll dive deeper into building RESTful APIs with Flask and connecting it all with persistent storage.
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