ETL and ELT are two approaches used in data integration and data warehousing to extract data from various sources, transform it into a suitable format, and load it into a target data repository.
ETL: extract, transform, load
ETL and ELT are two approaches used in data integration and data warehousing to extract data from various sources, transform it into a suitable format, and load it into a target data repository. Both methods serve the purpose of making data accessible and usable for analysis and reporting. However, they differ in the order of data processing steps.
Key differences and considerations
Data Volume and Processing Power: ETL is better suited for scenarios where data volumes are relatively smaller, and the data transformation requires substantial processing power. ELT, on the other hand, is designed to handle massive data volumes and takes advantage of distributed processing capabilities provided by big data technologies.
Data Latency: ETL may introduce some data latency as the transformation process occurs before loading data into the target repository. In contrast, ELT can provide near-real-time data access since loading happens first, and transformations occur later.
Data Governance and Security: ETL provides more control over data governance and security during the transformation phase. ELT may require additional attention to ensure data quality and security within the data repository itself.
Data Lake vs. Data Warehouse: ELT is commonly used in data lakes, where raw data is stored without significant structure or transformation, and processing occurs on-demand. ETL is often employed in traditional data warehousing, where data is transformed before being loaded into the warehouse.