Debugging Databricks Jobs Faster with VS Code + Remote Development

We’ve all been there: the grueling, soul-crushing ritual of debugging a Databricks job. You scroll through an endless sea of logs, hunting for a single error. You add a lonely print() statement, trigger a re-run, and then the worst part, you wait. You wait for the cluster to spin up, you wait for the cell to execute, only to realize you placed the print in the wrong spot. This loop isn’t just slow; it’s a productivity killer that makes even the best data engineers feel like they’re coding in slow motion.

That used to be my normal workflow too, a cycle of frustration that felt like coding with one hand tied behind my back. Everything changed when I transitioned to debugging Databricks jobs directly from Visual Studio Code using remote development.

By shifting to a more structured, local-first workflow, the difference in speed and clarity was immediate. It wasn’t just a minor improvement; it was a total paradigm shift in how I interact with my data pipelines.

This post breaks down what changed, why it matters, and how you can set up a similar approach to save yourself a lot of time.

The old way: debugging inside Databricks UI

Before switching to VS Code, my debugging loop looked like this:

• Run a job in Databricks Workflows
• Wait for it to fail
• Open job run details
• Scroll through logs in the UI
• Open notebooks attached to the job
• Add print statements or temporary fixes
• Re-run everything again

The biggest problems with this approach were:

• Slow iteration cycle (every change required a full job run)
• Limited debugging tools (no real breakpoints or step-through debugging)
• Hard-to-reproduce issues (especially cluster-specific bugs)
• Messy logging instead of structured inspection

It worked, but it was inefficient and frustrating when dealing with complex pipelines.

The shift: debugging from VS Code

The real productivity boost came when I started treating Databricks jobs like normal Python projects again.

With VS Code, I can:

• Run code locally or in a remote Databricks environment
• Use proper breakpoints and step-through debugging
• Inspect variables in real time
• Reproduce job logic without re-triggering full workflows
• Work with proper project structure instead of isolated notebooks

Most importantly: I stopped debugging “after failure” and started debugging “during development.”

Key enablers that made this possible

1. Databricks Asset Bundles

Using Databricks Asset Bundles (DAB), I was able to define jobs in a structured way (YAML-based), instead of clicking through UI definitions.

This gave me:

• Reproducibility
• Version control for jobs
• Easier local testing of job logic

2. Remote development in VS Code

Once I connected VS Code to Databricks compute via Databricks Connect, I could:

• Run the same code locally as in the cluster
• Attach debugger sessions
• Execute functions independently

Now instead of re-running a full job, I could do:

from etl.transform import clean_data

df = load_raw_data()
result = clean_data(df)
print(result.show())

No job submission required.

3. Structured logging instead of print debugging

In Databricks notebooks, I used to rely heavily on print() statements.

In VS Code, I switched to logging:

import logging

logger = logging.getLogger(__name__)

logger.info("Starting transformation step")
logger.debug(f"Input schema: {df.schema}")

This made it so much easier to:

• Filter logs
• Understand execution flow
• Trace issues in production runs

4. Breakpoints changed everything

This was the biggest productivity gain. Instead of guessing why a transformation failed, I could:

• Pause execution
• Inspect dataframe state
• Check intermediate transformations
• Evaluate conditions live

This alone eliminated hours of rerunning jobs.

5. Notebook output: Databricks vs VS Code

While Databricks notebooks are optimized for collaborative, massive-scale execution, VS Code offers a developer-centric environment, resulting in distinct output constraints that favor rapid local iteration.

Side-by-Side Comparison

While running unit tests in Databricks is possible, it is far less efficient. Databricks requires executing tests within jobs or notebooks on a cluster, which creates overhead like cluster startup time and slows down iteration. VS Code is superior for rapid development because it integrates directly with local test frameworks (like pytest or unittest), enabling instant execution, detailed failure output, and interactive debugging. Use VS Code for continuous testing and rapid development, and reserve Databricks for validating code in a distributed, production-like environment.

Why this approach is faster

Faster feedback loop

No cluster startup delays, no full job runs.

Better visibility

You see the data state at every step.

Local reproducibility

You isolate logic from infrastructure.

True debugging tools

Breakpoints will always be better than simple print statements.

Typical pitfalls in this transition

Neglecting code modularization

If your logic remains trapped in a single notebook, you won’t be able to fully leverage the debugging power of VS Code.

Relying on print statements

A combination of structured logging and a real debugger offers a much more scalable solution than basic print debugging.

Failing to ensure environment parity

Bugs will continue to evade detection if your local setup does not match the cluster environment.

Viewing Databricks solely as a notebook platform

While notebooks are excellent for discovery, debugging production-grade jobs effectively requires a more structured approach.

Recommended setup

If you want to replicate this workflow:

1. Use a proper project structure

project/
  src/
    etl/
      transform.py
      load.py
  tests/
  resources/
    databricks.yml

2. Use Databricks Asset Bundles

For job definitions and deployment consistency.

3. Enable remote debugging in VS Code

Depending on your setup:

• Databricks Connect
• SSH remote interpreter
• Container-based dev environment

4. Add logging everywhere meaningful

Avoid relying only on notebook outputs and print statements.

Final thoughts

Moving Databricks job debugging into VS Code fundamentally changed how I work.

Instead of treating failures as something I investigate after deployment, I now treat them as part of development. Caught early, inspected properly, and fixed quickly.

The biggest win wasn’t just speed. It was clarity.

When you can step through your pipeline like a normal Python application, Databricks stops feeling like a black box and starts feeling like a system you actually control.