Key differences:
- Transformation Stage: In ETL, transformation happens before loading, while in ELT, transformation happens after loading.
- Performance: ETL is often slower for large datasets since transformation is done in a separate staging area. ELT leverages the scalability of modern cloud platforms to perform transformations efficiently after loading.
- Data Processing Location: ETL typically processes data outside the target system (e.g., in a staging server), while ELT processes the data within the target system itself.
- Flexibility: ELT allows for storing raw data in the target system, providing more flexibility for future transformations and analyses, whereas ETL typically stores only processed and cleaned data.
Use Cases
- ETL: Best for traditional on-premises data warehouses where the data needs to be cleaned and processed before it is loaded.
- ELT: Ideal for cloud-based architectures, big data platforms, and modern analytics solutions like Azure Synapse Analytics or Snowflake.