SCD Type2 in pyspark
Slowly Changing Dimension (SCD) Type 2 is a technique used in data warehousing to maintain historical changes to dimension data over time. It involves creating new records in the dimension table whenever there is a change to an attribute value, thereby preserving historical information.
Here's a step-by-step guide to implementing SCD Type 2 in PySpark:
Read the historical and incremental data: Load your historical dimension data and the new incremental data into PySpark DataFrames.
Identify updates and inserts: Compare the historical and incremental data to identify which records need to be updated and which records need to be inserted as new versions.
Create new versions for updates: For records that need to be updated, create new versions with updated attribute values and validity dates.
Create new records for inserts: For new records, create entirely new entries with appropriate version numbers and validity dates.
Combine updates and inserts with historical data: Combine the updated records, new records, and existing historical data into a single DataFrame.
Aggregate to keep only the latest versions: Group the combined DataFrame by the key column (e.g., customer ID) and aggregate to keep only the latest version of each record.
Display or save the final DataFrame: Optionally, display or save the final DataFrame containing the updated dimension data.