ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two approaches for moving and preparing data for analysis, differing primarily in when and where data transformation occurs. In ETL, data is first extracted from source systems, transformed (cleaned, filtered, aggregated) in a staging area, and then loaded into a target database or warehouse. ELT reverses the order: raw data is loaded directly into the target system first, and transformations happen afterward using the processing power of the destination platform, such as a cloud data warehouse.
The key distinction lies in the flexibility and infrastructure requirements. ETL requires upfront planning to define transformation rules before loading, which works well for structured data with predictable schemas. For example, an ETL pipeline might validate customer addresses during transformation to ensure consistency before loading into a relational database. However, this approach can be rigid if business needs change. ELT, by contrast, defers transformation until after loading, leveraging scalable cloud systems like Snowflake or BigQuery to process raw data on demand. For instance, raw JSON logs from an application might be loaded into a data lake, then parsed and filtered later using SQL-based transformations. This flexibility suits unstructured data or scenarios where transformation logic evolves frequently.
The choice between ETL and ELT often depends on the tools and infrastructure available. Traditional ETL tools like Informatica or Talend are optimized for predefined workflows and work best when compute resources for transformations are limited. ELT relies on modern data platforms with high-performance query engines, enabling transformations using tools like dbt or Spark. For example, a team using AWS Redshift might load raw sales data and use SQL views to transform it dynamically, avoiding the need to reprocess data when requirements change. While ETL can introduce latency due to staging steps, ELT simplifies initial setup but requires robust governance to manage raw data quality. Developers should consider data volume, transformation complexity, and system capabilities when choosing between the two approaches.