What role does normalization or denormalization play in ETL transformations?

Normalization and denormalization in ETL transformations serve distinct purposes depending on the target system’s requirements. Normalization organizes data into structured, non-redundant formats by splitting it into smaller, related tables. This is common when loading data into transactional databases (OLTP systems) where write efficiency, data consistency, and update performance are critical. For example, during ETL, raw customer order data might be split into separate tables for customers, orders, and products, each linked by keys. Denormalization, conversely, combines tables to reduce query complexity and improve read speed, which is essential for analytical systems (OLAP) like data warehouses. For instance, sales data might be flattened into a single table with customer, product, and transaction details pre-joined to accelerate reporting.

A practical example of normalization in ETL is transforming a CSV file containing repeated customer addresses. The ETL process might create separate tables for customers and addresses, linked by a foreign key, to eliminate redundancy. In contrast, denormalization could involve merging product categories and sales records into a single dataset for a dashboard. This avoids costly joins during analysis, reducing latency for end users. Another example is a star schema in a data warehouse, where a central fact table (e.g., sales) is surrounded by denormalized dimension tables (e.g., time, product, store) to simplify queries.

The choice between normalization and denormalization hinges on the use case. Normalization ensures data integrity and efficient storage but can slow down complex queries due to joins. Denormalization sacrifices storage efficiency and increases redundancy to prioritize query speed. Developers must assess whether the target system requires frequent writes (favoring normalization) or fast reads (favoring denormalization). For instance, an e-commerce platform’s order-processing system would normalize data to handle concurrent transactions, while its reporting layer would denormalize data to accelerate sales trend analysis. Balancing these trade-offs during the ETL design phase is key to meeting performance and reliability goals.