ETL vs. ELT: How Do They Differ &Which Should Your Company Use?

Moving to a modern cloud data warehouse such as Snowflake, BigQuery, or Redshift involves decisions about building data pipelines, specifically choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) methods. ETL traditionally processes data by transforming it before loading into a warehouse, which can be time-consuming and costly, especially with large datasets and the need for extensive infrastructure maintenance. In contrast, ELT leverages cloud capabilities by loading data first and transforming it within the warehouse, allowing for more flexible and efficient data handling. As businesses increasingly rely on numerous SaaS applications, managing real-time structured and unstructured data at scale has become crucial, making ELT an attractive option due to its ability to minimize infrastructure overheads and adapt to dynamic analytical needs. When selecting between ETL and ELT vendors, companies should consider factors such as flexibility in managing multiple data sources, compatibility with their chosen cloud data warehouse, and pricing structures, which may vary based on integration levels, data rows, or data volume.