What are some common transformation patterns in ETL workflows?

ETL (Extract, Transform, Load) workflows rely on transformation patterns to convert raw data into formats suitable for analysis or reporting. Three common patterns include data cleansing, aggregation, and joining data from multiple sources. Data cleansing addresses inconsistencies like missing values, duplicates, or formatting errors. For example, a workflow might trim whitespace from text fields, convert dates to a standardized format (e.g., ISO 8601), or replace null values with defaults. Aggregation summarizes data, such as calculating total sales per month or average customer ratings. Joining combines datasets using keys—like merging customer orders with product details via a shared product ID—to create unified datasets for analysis.

Another set of patterns involves splitting or merging columns, data validation, and lookups. Splitting columns parses composite data into discrete fields—for instance, separating a “full_name” field into “first_name” and “last_name.” Validation ensures data meets predefined rules, such as checking if email addresses follow a valid format or flagging sales figures exceeding expected ranges. Lookups enrich data by referencing external tables, like translating country codes into full country names using a reference table. For example, a product database might use a lookup to replace a cryptic “category_id” with a readable category name stored in a separate metadata table.

More complex patterns include pivoting, deduplication, and handling slowly changing dimensions (SCD). Pivoting reshapes data, such as converting rows of monthly sales into columns for easier reporting. Deduplication identifies and removes redundant records—for example, merging duplicate customer entries by comparing names, emails, or addresses. SCD techniques manage historical changes in dimension tables, such as tracking address changes for customers over time. A Type 2 SCD, for instance, might create new rows with timestamps for each change, preserving historical context. These patterns ensure data remains accurate, consistent, and aligned with business needs throughout its lifecycle.