ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration processes used to move and transform data from one system to another.

The main difference between ETL and ELT is the order in which the data is processed.

In ETL, data is extracted from one or more sources, transformed to fit the target system's schema, and then loaded into the target system.

In ELT, data is extracted from one or more sources and loaded into the target system before it is transformed to fit the target system's schema.

When to use ETL vs. ELT?

It's worth noting that ETL and ELT are not mutually exclusive and can be used together in a hybrid approach.

A hybrid approach to ETL and ELT can be implemented in a few different ways, depending on the specific requirements of the organization and the data integration project.

Real-time vs. batch processing

If real-time data processing is a priority, ELT may be a better option as it allows for near-instant data loading and processing. ETL, on the other hand, is better suited for batch processing of data on a schedule.

Data volume and complexity

ELT is well suited for handling large volumes of data, such as data from social media or IoT devices, and can handle data in various formats and structures. ETL is better suited for handling structured data from traditional sources, such as relational databases.

Data transformation

ELT is designed to handle simple data transformations, such as data cleaning and validation, while ETL is designed to handle more complex data transformations, such as data mapping and aggregation.

Data quality and governance

ETL is typically used when data quality and governance is a top priority as it allows for the preprocessing of data before it is loaded into the target system. ELT, on the other hand, may not have the same level of data quality and governance capabilities.

Cost and resources

ELT is generally less expensive and requires fewer resources than ETL. ELT uses the processing power of the target system to perform data transformations, while ETL requires a separate system for data processing.

Hybrid approach

1. ELT for real-time data and ETL for historical data: In this approach, ELT is used to handle real-time data streams and load them into the target system in near real time. ETL is then used to process historical data in batches and load it into the target system on a schedule. This allows organizations to take advantage of the real-time capabilities of ELT while still maintaining a historical record of their data.

2. ELT for simple transformations and ETL for complex transformations: In this approach, ELT is used to handle simple data transformations, such as data cleaning and validation, while ETL is used to handle more complex data transformations, such as data mapping and aggregation. This allows organizations to take advantage of the performance benefits of ELT while still being able to handle more complex data transformation needs.

3. ELT for big data and ETL for structured data: In this approach, ELT is used to handle big data, such as data from social media or IoT devices, and load it into the target system. ETL is then used to handle structured data, such as data from a relational database, and load it into the target system. This allows organizations to take advantage of the scalability and flexibility of ELT for big data while still being able to handle structured data more traditionally.

Tools

ETL

• Informatica PowerCenter

• IBM DataStage

• Microsoft SQL Server Integration Services (SSIS)

ELT

• Apache NiFi

• Apache Kafka

• AWS Glue

The tools mentioned here are pure ELT or ETL tools. You can also use General purposes tools like Airflow/Prefect/Spark to achieve ELT/ETL operations.