Use modular, parameterized code with reusable functions and notebooks for faster development. Separate environments for dev, test, and prod ensure stability. Leverage Databricks’ Job clusters, Delta Live Tables, and Autoloader for efficiency. Enable detailed Spark logs and Ganglia metrics for debugging. Use display(), df.explain(), and Spark UI for performance insights. Implement unit tests with pytest and assertions in notebooks. For collaboration, use Git integration, branching, and Databricks Repos. Cache intermediate data wisely and optimize joins, shuffles, and partitions. Adopt CI/CD for deployment to streamline the Spark job lifecycle from prototype to production.