📝 Trunk-Based Development for Data Engineers: Powerful, but Not So Simple

Trunk-Based Development (TBD) is a Git workflow where all developers collaborate on a single shared branch, typically main or master. It encourages small, incremental changes that are merged back frequently — often several times per day — and is a foundational practice for high-velocity teams embracing continuous integration and continuous delivery (CI/CD).

🚀 Who Uses Trunk-Based Development?

TBD is widely adopted by application engineering teams at high-performing tech companies like Google, Facebook, and Netflix. These teams:

• Deploy code to production multiple times per day.

• Rely on robust CI/CD pipelines with automated testing, linting, and security scanning.

• Use feature flags to safely release incomplete features to production.

• Optimize for speed and feedback loops over isolated development.

🔧 How Trunk-Based Development Works

• Developers commit code directly to main or create short-lived feature branches that are merged back quickly (usually within a day).

• Feature flags are used to hide unfinished work from users while still integrating code early.

• Each commit triggers CI tests, ensuring code quality.

• Deployments are often automated and happen multiple times a day.

✅ Pros of Trunk-Based Development

• Fast integration and feedback: Prevents divergence and reduces complex merge conflicts.

• Improved release velocity: Ideal for teams practicing continuous deployment.

• Simpler Git history: Fewer long-lived branches to manage.

• Encourages small, modular commits: Promotes good engineering discipline.

⚠️ Cons & Challenges

• Requires strong testing practices — weak test suites will lead to breakages.

• Feature flags add operational complexity and need to be maintained.

• Hard to manage in monolithic codebases or legacy systems.

• Not well-suited for teams without CI/CD maturity.

🤖 What About Data Engineering & DataOps Teams?

While TBD is commonplace in app development, it’s less widely adopted in data teams — but that’s starting to change.

From my experience, most data teams still use a Git Flow model:

• Long-lived dev, staging, and main branches

• Features are developed in separate branches and merged upward

• Releases are often batched or manually coordinated

This works well for teams that:

• Deploy less frequently

• Rely on scheduled batch processing

• Have limited automation or test coverage

But this model also introduces:

• Merge drift (where dev diverges significantly from main)

• Large/Long-lived PRs that are hard to review and merge cleanly

• Delayed feedback loops and increased coordination overhead

Why It’s Harder to Adopt TBD in Data

• Many data pipelines are batch-based, not real-time, so feedback is slower.

• CI/CD for data (e.g., dbt, Airflow) is still catching up to application tooling.

• Testing data changes is non-trivial: you may not know something broke until a table is empty or a report is wrong.

• Legacy data platforms may lack good environment isolation or support for feature flagging.

But It’s Becoming More Viable

• Tools like dbt Cloud, Datafold, and Great Expectations bring testing and CI to the data world.

• Versioned data pipelines and Git-based orchestration (e.g., with Airflow, Dagster, Prefect) enable safer adoption.

• Data teams working in modern cloud environments (Snowflake, BigQuery, Databricks) can start mimicking app-team practices.

🧠 When to Consider Trunk-Based Development

✅ You’re ready for TBD if:

• You have automated testing and CI pipelines in place.

• Your team merges code multiple times per day.

• You use (or are willing to adopt) feature flags.

• You prioritize speed and fast iteration.

❌ Hold off if:

• You’re still working with long-lived batch jobs and no automated testing.

• Your dev environments are shared or fragile.

• You’re managing data manually in production.

🧩 Final Thoughts

Trunk-Based Development is a powerful strategy that aligns beautifully with modern software delivery practices. While it’s most effective for application engineering teams, forward-thinking DataOps and ML Engineering teams can also benefit — especially as testing, CI/CD, and infrastructure-as-code become standard in the data world.

Even if full TBD feels out of reach today, adopting its core principles — like small PRs, short-lived branches, and fast feedback — can meaningfully improve your team’s velocity and quality.