Refactoring your folder structure and naming conventions for Medallion architecture pipelines is an essential step to keep code maintainable and intuitive. Based on your context, shifting these pipelines from notebook/ to src/ is a solid move, especially as your repository now contains more differentiated pipeline logic—including old-to-new architecture transformations and various processing routines for Databricks.

Folder Placement: notebook/ vs src/

• Placing pipelines in src/ aligns with standard Python project structures where core logic lives under src/ and interactive/experimental code, like notebooks, goes in notebook/.

• Pipelines for both Medallion (Bronze/Silver/Gold) and non-Medallion architectures are first-class production code, so keeping them together in src/ is logical.

• For migration pipelines (old architecture → Medallion), it's best to house them in src/ alongside other ETL jobs, perhaps in a subfolder (e.g., migration/ or legacy_ingest/) if they grow in number.

Naming and Structure Suggestions

Your current naming (e.g., 00_bronze_to_01_silver_pt1.py) is clear about the flow but could be streamlined. Here are options:

1. Migration/Transformation Specific Pipelines

If possible, encapsulate old-to-new transformation steps in a dedicated subfolder:

Alternatively, prefix migration scripts with migration_ or legacy_.

2. Bronze Layer Pipelines with Scheduling

Your naming scheme (00_bronze_fir_data_pipeline.py, scheduling notes in comments/docs) is clear for newcomers. However, including schedule information in the file name itself can be noisy unless scheduling is a primary distinguishing factor.

Suggestions:

• Stick with pipeline-specific names, and document the schedule in a config file (YAML, JSON, etc.), orchestration docs, or in pipeline code comments.

• If schedules are a strong part of the identity, consider a naming template like:

   

  text

  bronze_fir_daily_pipeline.py bronze_tiny_daily_pipeline.py bronze_huge_daily_pipeline.py

  But avoid having time-of-day in the filename (e.g., _1am_), as that can quickly become brittle as schedules change.

3. More Intuitive Naming for Pipelines

• Use names reflecting the data processed (e.g., bronze_orders_ingest.py), source, or domain instead of times.

• Place all Bronze pipelines in a folder:

   

  text

  src/ |-- bronze/ |-- fir_data_pipeline.py |-- tiny_data_pipeline.py |-- huge_data_pipeline.py

  Then document schedules elsewhere.

Refactoring Migration Pipelines

• Split transformation steps into composable functions or classes for clarity and reuse.

• If steps are chained, consider a driver script or orchestration (Airflow, Databricks Jobs) that sequences them.

• Use descriptive function names within modules:

   

  text

  def extract_legacy_bronze(): ... def transform_to_silver(): ...

  This makes the intent clear to future readers.

Summary Table

Recommendations

• Move all ETL pipelines to src/ regardless of architecture.

• Organize migration scripts in a clear subfolder or with consistent prefix.

• Name pipelines for their data/topic, not for their schedule or execution time.

• Use documentation and metadata/configs to record scheduling, not filenames.

This restructuring and naming strategy will help your repo scale as your team and pipeline complexity grow.