Iām 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 Iām 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.
Iām 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.
