Slowly changing dimensions (SCDs) are a common challenge in extract, transform, load (ETL) processes when dealing with data that changes over time. SCDs refer to dimensions in a data warehouse that have values that change over time, and it’s important to handle these changes effectively to maintain data integrity. In this blog post, we will explore some techniques for handling SCDs in ETL processes.

1. Type 1 Slowly Changing Dimensions

A Type 1 SCD approach involves overwriting the existing dimension data with the new data. This means that historical data is not preserved, and only the most recent values are stored. This approach is suitable when historical changes are not important, and the current values are all that matter. The ETL process simply replaces the old value with the new value, without any tracking or auditing.

def handle_type1_scd(new_data, dimension_table):
    # Check if dimension table already has the new data
    if new_data['id'] not in dimension_table['id']:
        # Overwrite existing dimension data with new data
        dimension_table.loc[dimension_table['id'] == new_data['id']] = new_data

2. Type 2 Slowly Changing Dimensions

A Type 2 SCD approach involves creating new records for each change, while preserving the historical data. Each record in the dimension table is assigned a unique identifier, and a start and end date to indicate the validity period. New records are inserted for any changes, and the end date of the previous record is updated to mark the end of its validity. This approach allows for historical analysis and tracking changes over time.

def handle_type2_scd(new_data, dimension_table):
    # Check if dimension table already has the new data
    if new_data['id'] not in dimension_table['id']:
        # Mark the end date of the previous record
        dimension_table.loc[dimension_table['id'] == new_data['id'], 'end_date'] = new_data['start_date']

        # Add new record with unique identifier and start date
        new_record = new_data.copy()
        new_record['id'] = generate_unique_id()
        new_record['start_date'] = get_current_date()

        # Insert new record into dimension table
        dimension_table.append(new_record, ignore_index=True)

3. Type 3 Slowly Changing Dimensions

A Type 3 SCD approach involves adding extra columns to the dimension table to store limited historical data. These additional columns capture a few previous values of the dimension, allowing for some historical analysis. This approach strikes a balance between preserving historical data and keeping the dimension table lean.

def handle_type3_scd(new_data, dimension_table):
    # Check if dimension table already has the new data
    if new_data['id'] not in dimension_table['id']:
        # Update limited historical values in the dimension table
        dimension_table.loc[dimension_table['id'] == new_data['id'], 'previous_value_1'] = dimension_table[new_data['id']]
        dimension_table.loc[dimension_table['id'] == new_data['id'], 'previous_value_2'] = dimension_table['previous_value_1'] 

        # Overwrite existing dimension data with new data
        dimension_table.loc[dimension_table['id'] == new_data['id']] = new_data

In conclusion, properly handling slowly changing dimensions in ETL processes is crucial for maintaining data integrity and enabling historical analysis. The approach taken may depend on the specific requirements and constraints of the data warehouse. Implementing the appropriate technique, whether it’s Type 1, Type 2, or Type 3, ensures that the data is accurately captured and preserved in a way that satisfies business needs.

#datawarehouse #ETL