ETL vs. ELT: Which Approach to Choose in Modern Data Warehouses?

Evolution of Data Transformation: ETL and ELT

The ETL (Extract, Transform, Load) approach, which has been accepted as the standard in the data engineering world for years, was designed according to the limits of on-premise hardware. The data source was removed from the system, converted on an intermediate server (Transformation engine) and then loaded into the Data Warehouse (DWH).

ELT Revolution with Cloud Computing

Thanks to the huge computing power offered by cloud data warehouses (Cloud Data Warehouses), transformation costs have become cheaper. This situation gave birth to the ELT (Extract, Load, Transform) architecture. Loading the data in its raw form (Raw Data) into the target system and performing the transformations through the data warehouse's own SQL processor has become a much more agile and faster method.

As DVision Technology, we offer a hybrid approach to data engineers with the Microto platform. We enable you to manage both traditional SSIS transformations and dynamic SQL scripts running on the target data warehouse from a single orchestration center. You can either load your data by transforming it (ETL) or transform it after loading (ELT).