Best Development Strategies for Building Reusable Data Engineering Components in Databricks

[tarunnagar]

I’m looking to gather insights from data engineers, architects, and developers who have experience building scalable pipelines in Databricks. Specifically, I want to understand how to design, implement, and manage reusable data engineering components that can be leveraged across multiple ETL/ELT workflows, machine learning pipelines, or analytics applications.

Some areas I’m hoping to explore include:

• Modular pipeline design: How do you structure notebooks, jobs, and workflows to maximize reusability?
• Reusable libraries and functions: Best practices for building common utilities, UDFs, or transformation functions that can be shared across projects.
• Parameterization and configuration management: How do you design components that can handle different datasets, environments, or business rules without rewriting code?
• Version control and CI/CD: How do you maintain, test, and deploy reusable Databricks components in a team environment?
• Integration with other tools: How do you ensure reusable components work well with Delta Lake, MLflow, Spark, and other parts of your data stack?
• Performance and scalability considerations: How do you build reusable components that perform well for both small datasets and large-scale data pipelines?
• Lessons learned and pitfalls to avoid: Common mistakes when trying to build reusable components and how to address them.

I’m seeking practical, real-world strategies rather than theoretical advice. Any examples, patterns, or recommendations for making Databricks pipelines more modular, maintainable, and reusable would be extremely valuable.

[ShaneCorn]

To build reusable data engineering components in Databricks, focus on modular design by creating reusable notebooks, libraries, and widgets. Leverage Delta Lake for data consistency and scalability, ensuring reliable data pipelines. Use MLflow for model tracking and deployment, promoting reusability in machine learning workflows. Implement version control using Git to manage notebook changes. Additionally, standardize data transformation logic in Python or Scala libraries for easy reuse across different projects and teams, improving efficiency and collaboration.

[jameswood32]

ChatGPT said:

A common community strategy is to treat Databricks assets like a shared engineering product. Build modular, parameterized notebooks or Python packages, publish them to a central repo (Git + CI/CD), and version them just like application code. Use Delta Live Tables or workflow jobs for standardized patterns—ingest, validate, transform—and wrap repeated logic in Unity Catalog–managed functions/libraries. Enforce data contracts, add automated tests with pytest, and maintain clear docs so teams can plug components into new pipelines with minimal friction.

[mariadawson]

To build reusable data engineering components in Databricks, focus on modular design by creating testable Python/Scala libraries instead of relying on %run notebooks. Parameterize all notebooks using widgets for dynamic execution across environments. Leverage Delta Lake and Unity Catalog for consistent data governance and shared access across pipelines. Implement rigorous version control using Databricks Repos and Git, backed by a CI/CD process that automates testing, builds library artefacts, and deploys job configurations. This approach standardizes data transformation logic and improves collaboration and pipeline resilience.

[Davidwilliamkt]

The best strategy is to build modular, parameterized, Delta-optimized functions and package them into reusable Python modules, while keeping Databricks notebooks only for orchestration. This creates consistent, scalable, and easily maintainable data engineering pipelines.