Understanding ELT & ETL
ETL (Extract, Transform, Load) has been the traditional approach for data warehousing and analytics for the past couple of decades. The ELT (Extract, Load, Transform) approach changes the old paradigm as the transform and load are being switched.
Both the ETL and ELT solve the same need as they help to clean, manage the raw data by readying them to be analyzed. Billions of data and events are needed to be collected, processed, and analyzed by businesses by enriching, molding, and transforming them to make them meaningful.
The working methodologies of both these tools are different and this leads to new possibilities in many modern data projects. These differences include how raw data is managed when processing is done and how analysis is performed.
Let us discuss, the technological differences between ETL and ELT showing data engineering and analysis in these approaches of the three stages: E, T, L:
1. Extraction
It involves retrieving raw data from an unstructured data pool and migrating it into a temporary, staging data repository.
2. Transformation
This stage includes converting, structuring, and enriching the raw data to match the target source.
3. Loading
It involves loading the structured data into a data warehouse to be analyzed and used by BI tools.
