novatechflow | Alexander Alten

This article explains how the rise of cloud data warehouses and exploding data volumes have shifted the industry from traditional ETL toward ELT as the new default for modern pipelines. ETL still excels when complex, compliance-critical transformations must happen before data lands in a warehouse, but its batch nature, rigidity and scaling limits make it increasingly unsuited for today’s diverse, high-volume datasets. ELT flips the model: load raw data first, transform later using the elastic compute power of platforms like Snowflake, BigQuery or Redshift. This brings faster ingestion, higher flexibility, lower cost, and better alignment with DataOps and AI-driven analytics. The article breaks down pros, cons and use cases for both approaches, emphasizing that ELT is generally the strategic choice for big data, real-time analytics and future-proof architectures—while ETL remains valuable for small, structured, compliance-heavy environments. ETL has been the traditional approach, whe...