What is ELT

ELT (Extract, Load, Transform) is a data integration process that involves extracting data from different source systems, loading it into a data warehouse, and then transforming it to meet the requirements of analytical applications. Modern data warehouses are highly optimized for performance and can handle large-scale data transformations. This is in contrast to the ETL approach, where data is transformed before being loaded, often using external processing power. By moving transformations into the data warehouse, ELT can significantly reduce data latency and offer enhanced performance, especially when handling large datasets.

Components of ELT

ELT encompasses three main stages:

1. Extract
   The extraction phase involves pulling data from various source systems which can include databases, files, and APIs. The aim is to retrieve all the necessary data, often from heterogeneous sources, and prepare it for integration into the data warehouse.

2. Load
   Once the data has been extracted, the next step is to load it into the data warehouse. This involves ensuring that the data is transferred efficiently and securely. The loading phase is critical as it lays the foundation for the subsequent transformation of the data.

3. Transform
   After the data has been loaded into the data warehouse, the transformation phase begins. Here, the data undergoes various manipulations to ensure that it is in the right format, structure, and quality for analytical processes. This could include tasks like filtering, aggregation, and joining datasets.

Comparing ELT with ETL

In the data integration landscape, ELT and ETL are two prominent methodologies. While they may seem similar at first glance, the difference in the order of the 'Load' and 'Transform' stages has significant implications for how data is processed and integrated.

Advantages of ETL

• Data Quality
  Since data is transformed before it is loaded into the data warehouse, there’s an opportunity for extensive data quality checks.

• Compatibility with Legacy Systems
  ETL has been around for a long time, making it more compatible with legacy systems that may not handle in-database processing efficiently.

• Lower Storage Requirements
  Because data is cleaned and transformed before loading, it often requires less storage space in the data warehouse.

Advantages of ELT

• Processing Speed
  ELT can be faster because it leverages the processing power of modern data warehouses, which is often more scalable than the processing power available in intermediate staging areas.

• Simplified Workflow
  By reducing the need for a separate transformation area, the ELT process can be less complex and easier to manage.

• Real-time Processing
  ELT is better suited for environments where near real-time data is needed, as the data can be quickly loaded into the data warehouse and then transformed as needed.

Considerations

• Data Volume and Complexity
  ELT is generally better suited for large datasets and complex transformations, while ETL may be more appropriate for smaller datasets.

• Data Warehouse Capabilities
  The efficiency of the ELT process depends on the capabilities of the data warehouse, and may not be optimal if the data warehouse does not have sufficient processing power.

• Data Security and Compliance
  In some cases, data must be anonymized or encrypted before being loaded into a data warehouse for compliance reasons, which may make ETL more appropriate.