Why Do We Prefer ELT Rather than ETL in the Data Lake? What is the Difference between ETL & ELT
insideBIGDATA, Tuesday, July 4,2023
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two different types of processes to move data from a source system to a destination system. ETL extracts raw data from a source and transforms it into a structured format and then loads it into a destination system.
Transformation takes place on a secondary processing server before loading data into a destination system. However, ELT extracts data from the source and loads it directly into a destination system. Transformation takes place in a destination system or a database.
ETL is in practice for over 20 years and is best for small datasets that require complex transformations. It also maintains the privacy and security of data. ELT is newer than ETL and ideal for large datasets that require high speed and efficiency. ELT is compatible with data lakes due to its ability to handle large and unstructured datasets. Selecting the appropriate method depends on factors, such as data volume, speed, privacy concerns, and maintenance costs.