As part of Day 3 of Phase 1: Better Data Engineering in the Databricks 14 Days AI Challenge – 2 (Advanced), the focus moved toward understanding job orchestration and preparing notebooks for automated execution.
The notebook was first enhanced by introducing widget parameters to support runtime configuration. This allowed the workflow to remain flexible and reusable instead of relying on hardcoded execution logic.
The feature engineering logic developed earlier was then modularized into a function. Organizing transformations this way improved readability and made the notebook better suited for pipeline-based execution.
Following this, a Job was created using the workflow interface in Databricks. The notebook was added as a task, parameters were passed through configuration, and a daily schedule was defined to automate execution.
During implementation, ChatGPT supported the process as a technical reference for validating orchestration concepts and notebook structuring decisions.
This exercise helped demonstrate how data workflows evolve from manual notebook runs into repeatable and scheduled data engineering pipelines.
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