When to Choose ETL vs. ELT for Maximum Efficiency
ETL has been the traditional approach, where data is extracted, transformed, and then loaded into the target database. ELT flips this process - extracting data and loading it directly into the system, before transforming it. While ETL has been the go-to for many years, ELT is emerging as the preferred choice for modern data pipelines. This is largely due to ELT's speed, scalability, and suitability for large, diverse datasets generated by multiple different tools and systems, think about CRM, ERP datasets, log files, edge computing or IoT. List goes on, of course.. Data Engineering Landscape Data engineering is the new kind of DevOps. With the exponential growth in data volume and sources, the need for efficient and scalable data pipelines and therefore data engineers has become the new standard . In the past, limitations in compute power, storage capacity, and network bandwidth made the famous 3-word "let's move data round" phrase Extract, Transform, Load (ETL) the