Hey Devs 👋
Sometimes in data engineering, the biggest lessons don't come from huge systems or fancy architectures…
They come from something simpler:
Switching between languages.
Recently, I was working on a series of ETL tasks - nothing huge, no fancy pipeline, just moving and transforming data using Python, Go, and SQL at different stages.
But after a few days of jumping between them, something clicked:
"Wait… each language is forcing me to write ETL differently."
And that thought basically became the reason for this post.
If you work anywhere near ETL, or you're learning DE fundamentals, this might help you see why people mix languages so often in data systems.
🧩 The Trigger: A Simple Task That Taught Me 3 Mindsets
It started with a small exercise:
- clean raw data → Python
- push the processed data efficiently → Go
- shape it inside the database → SQL
The task itself wasn't special - but how each language made me think was completely different.
And that's what made this interesting.
🐍 Python: The Language of Expressive Transformations
Python makes ETL feel fluid.
Whether you're cleaning rows, normalizing schema, or converting CSV → Parquet, Python encourages:
- readable transformations
- fast experimentation
- handling messy data
- “write it like you'd explain it”
It’s the place where ETL logic is easiest to express.
Mindset when using Python:
“Let me write something clear that handles all edge cases.”
You focus on correctness and clarity - not performance.
Perfect for:
- feature engineering
- data wrangling
- format conversions
- enrichment steps
⚡ Go: The Language That Forces Structure and Performance
Then comes Go.
When the job is to move data fast - not just transform it - Go shines.
It pushes you toward:
- strict typing
- predictable memory
- batch processing
- concurrency patterns
- controlled error handling
It basically rewires your ETL brain:
“Make it efficient, make it consistent, make it fast.”
Where Python feels like storytelling, Go feels like construction.
Perfect for:
- ingestion scripts
- connectors
- bulk inserts
- CLI tools for data movement
(And if you're inserting into something like ClickHouse, Go feels very natural.)
🧾 SQL: The Language of Intent, Not Procedure
SQL reminds you that ETL isn't always about writing code step-by-step.
Sometimes you simply:
- describe the shape of the data
- describe the relationships
- describe the clean output you want
…and the engine handles the rest.
SQL forces a different mental model:
“Tell the database what result you need - not how to compute it.”
Perfect for:
- deduplication
- aggregations
- joining standardized tables
- shaping analytical datasets
Often, SQL replaces an entire chunk of Python code with a single, elegant SELECT.
🧠 The Bigger Learning: Clean ETL Isn’t About One Language
People often ask:
- “Is Python enough for ETL?”
- “Should I switch to Go?”
- “When should SQL handle transformations?”
But after trying to mix all three, the truth feels much simpler:
Clean ETL comes from combining the strengths of each language.
- Use Python for transformations
- Use Go for ingestion + performance
- Use SQL for modeling + shaping results
You don't need a giant pipeline to learn this - even a small task teaches the distinction clearly.
💬 Your Turn
If you've ever switched between these languages for ETL, I’d love to hear:
- What felt natural?
- What felt painful?
- Which language pushed you toward cleaner design?
And if you're learning data engineering - try a small multi-language ETL experiment.
You'll learn more from that than any tutorial.
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