🚀Git + Databricks: Why Both Are Essential for Modern Data Engineering

Not long ago, I was working on a PySpark pipeline inside Databricks.
It was smooth, fast, and collaborative — and I thought to myself: “Databricks has versioning, so why do we even need Git?”

But the deeper I went into real-world data projects, the more I realized this:
👉 Databricks versioning is powerful for notebooks, but Git is irreplaceable for software-grade collaboration.

Let’s dive in.

📌 The Magic of Git
When you’re part of a team, Git isn’t just “nice to have” — it’s your safety net.

Here’s why:

1️⃣ Branching & Collaboration

Git allows multiple engineers to work on features simultaneously using branches.
Merge, compare, and resolve conflicts without breaking production code.
2️⃣ Code Reviews & Pull Requests

Databricks notebooks have version history, but they don’t provide the structured workflow of PRs, reviews, and approvals.
Git ensures that every line of code has accountability.
3️⃣ Integration with CI/CD

Git hooks into tools like GitHub Actions, Azure DevOps, or Jenkins.
That means your Databricks notebooks can become part of an automated testing and deployment pipeline.
4️⃣ Portability & Backup

With Git, your code isn’t locked inside Databricks.
You can clone, move, or share repositories across teams and organizations.
💡 In short: Git makes your project software-engineering ready.

📌 The Strength of Databricks
Now, let’s not underestimate what Databricks brings to the table:

1️⃣ Notebook Versioning

Every edit you make is saved — you can roll back to previous versions without fear.
2️⃣ Real-Time Collaboration

Think Google Docs for data pipelines. Multiple engineers can co-edit a notebook and see updates live.
3️⃣ Integrated Runtime & Execution

Unlike Git, Databricks doesn’t just track code — it actually executes it on clusters.
That means version history includes not only the code, but the runtime context.
4️⃣ UI for Data Teams

Not every data engineer is a Git wizard. Databricks versioning provides a low-barrier entry point for tracking changes.
🌟 The Best of Both Worlds
Here’s the truth:

Databricks versioning = great for quick collaboration and small changes.
Git = essential for large-scale projects, production pipelines, and enterprise-grade workflows.
Together, they create a workflow that’s both agile and reliable:

Experiment in Databricks notebooks with built-in versioning.
Push stable code to Git for collaboration, reviews, and CI/CD.
Deploy seamlessly with confidence.
Let me tell you something.

In one of my projects, we had 5+ engineers working on a single ETL pipeline.

Without Git, we kept overwriting each other’s changes inside notebooks. Chaos! 😅
Once we integrated Git, we could branch, review, and merge cleanly — while still enjoying Databricks’ notebook history for small fixes.
The result?
⚡ Faster collaboration
⚡ Fewer production bugs
⚡ A happier engineering team

So, why Git if Databricks already has versioning?
👉 Because Git brings discipline, structure, and scalability, while Databricks brings collaboration and execution power.

Think of it this way:

Databricks is your playground 🎢
Git is your safety harness 🛡️
Together, they ensure you can build, experiment, and scale with confidence.

💡 My advice: If you’re starting with Databricks, enjoy its versioning — but don’t skip Git. Master both, and you’ll be unstoppable in your data engineering career. 🚀