SQL vs Python in Data Pipelines

SQL has long been the go-to tool for everyone from old-school DBAs to new-school Data Engineers. Python, meanwhile, complements SQL like a bread bag for a sandwich—or is it the other way around?

We’re likely destined to use both SQL and Python, but the real question is: when should you use each?

At the start of a project, the decision seems straightforward. But as the codebase grows, things get murky, and often the balance tips. You may wake up one day realizing that your codebase is now:

• 80% SQL
• 80% Python

The key to good pipeline development is moderation. Use both SQL and Python where appropriate to avoid becoming a one-trick pony.

Python: When to Use It

• Complex business logic
• In-memory computations
• Machine Learning tasks
• Multiple computational layers

SQL: When to Use It

• Tabular data
• CRUD operations
• Analytics
• Few large computations

Making the Right Choice

If you’re dealing with structured data from an RDBMS with standard business requirements, SQL is the obvious choice. It’s designed for joins, rollups, and analytics. For example, if you have two large datasets in S3 and need to merge them with rollup analysis, SQL will provide a cleaner, more efficient solution.

Conversely, if your task involves complex data transformations, such as feature engineering for ML models, Python is the way to go. The flexibility it offers for detailed transformations and unit tests makes it ideal for these tasks.

Don’t Let Convenience Dictate Your Tools

While it’s tempting to use what your team is comfortable with, this mindset can lead to bad decisions. Always let technical requirements drive your choice.

SQL Pitfalls to Avoid

• Spaghetti queries
• Lack of unit testing
• Scattered business logic
• No standards for SQL writing (e.g., dbt)
• Non-idempotent queries

Python Pitfalls to Avoid

• Lack of unit testing
• Messy code without standards
• Long, complex methods (50+ lines)
• Poor dependency management
• Bad development environments

While SQL can get out of control with complex queries, Python pipelines can become an unmanageable mess if clean coding practices aren’t followed.

Closing Thoughts

In the SQL vs Python debate, it’s not one or the other—it’s both, at the right time. If you examine your project’s requirements closely, it’s often easier than expected to determine which tool to use.

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