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Removing columns

Removing columns before loading data into a database is a reliable method to eliminate sensitive or unnecessary fields. For example, in the given scenario, a source is created with a "country_id" column, which is then excluded from the database before loading.

Let's create a sample pipeline demonstrating the process of removing a column.

  1. Create a source function that creates dummy data as follows:

    import dlt

    # This function creates a dummy data source.
    def dummy_source():
    def dummy_data():
    for i in range(3):
    yield {"id": i, "name": f"Jane Washington {i}", "country_code": 40 + i}

    return dummy_data()

    This function creates three columns id, name and country_code.

  2. Next, create a function to filter out columns from the data before loading it into a database as follows:

    from typing import Dict, List, Optional

    def remove_columns(doc: Dict, remove_columns: Optional[List[str]] = None) -> Dict:
    if remove_columns is None:
    remove_columns = []

    # Iterating over the list of columns to be removed
    for column_name in remove_columns:
    # Removing the column if it exists in the document
    if column_name in doc:
    del doc[column_name]

    return doc

    doc: The document (dict) from which columns will be removed.

    remove_columns: List of column names to be removed, defaults to None.

  3. Next, declare the columns to be removed from the table, and then modify the source as follows:

    # Example columns to remove:
    remove_columns_list = ["country_code"]

    # Create an instance of the source so you can edit it.
    data_source = dummy_source()

    # Modify this source instance's resource
    data_source = data_source.dummy_data.add_map(
    lambda doc: remove_columns(doc, remove_columns_list)
  4. You can optionally inspect the result:

    for row in data_source:
    #{'id': 0, 'name': 'Jane Washington 0'}
    #{'id': 1, 'name': 'Jane Washington 1'}
    #{'id': 2, 'name': 'Jane Washington 2'}
  5. At last, create a pipeline:

    # Integrating with a dlt pipeline
    pipeline = dlt.pipeline(
    # Run the pipeline with the transformed source
    load_info =

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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