Key Differences Between ETL & ELT and which is better?

Posted by Archi Jain on December 28th, 2023

ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform)

Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two data integration approaches that play pivotal roles in modern data warehousing and analytics. Understanding the key differences between them is crucial for making informed decisions based on specific business requirements.

ETL (Extract, Transform, Load):

1. Process Flow:

• Extract: Data is extracted from source systems.

• Transform: Data undergoes transformation in a dedicated staging area or ETL server.

2. Performance:

• Transformation is typically performed in a separate ETL server before loading into the data warehouse.

• Suitable for scenarios where the source data needs significant restructuring before being stored in the data warehouse.

3. Data Warehouse Independence:

• Data transformation occurs before loading into the data warehouse.

• ETL processes are designed to be independent of the target data warehouse.

4. Schema Evolution:

• ETL processes handle schema transformations before loading data into the warehouse.

• Changes in the data warehouse schema may require modifications to the ETL process.

5. Use Cases:

• Well-suited for scenarios where data needs extensive cleaning, transformation, or enrichment before being stored in the data warehouse.

• Historically prevalent in traditional data warehousing.

ELT (Extract, Load, Transform):

1. Process Flow:

• Extract: Data is extracted from source systems.

• Load: Raw data is loaded into the data warehouse.

• Transform: Transformation occurs within the data warehouse using its processing power.

2. Performance:

• Transformation is performed using the computational capabilities of the data warehouse.

• Ideal for scenarios where the data warehouse has robust processing capabilities.

3. Data Warehouse Independence:

• Transformation is inherently linked to the data warehouse capabilities.

• ELT processes are tightly integrated with the data warehouse.

4. Schema Evolution:

• The data is initially loaded without significant transformation.

• Transformation occurs within the data warehouse, making it adaptable to changes in the warehouse schema.

5. Use Cases:

• Suited for scenarios where the data warehouse is powerful enough to handle complex transformations.

• Gaining popularity in the era of cloud-based data warehouses and big data analytics.

Considerations for Choosing Between ETL and ELT:

1. Data Volume:

• For large volumes of data, ELT may be more efficient as it leverages the processing power of the data warehouse.

2. Data Complexity:

• ETL is preferred when extensive data cleaning and transformation are required before loading into the data warehouse.

3. Data Warehouse Capabilities:

• ELT is advantageous when using a data warehouse with robust processing capabilities, such as cloud-based solutions.

4. Flexibility:

• ELT is more flexible in adapting to changes in data warehouse schemas.

5. Integration Requirements:

• Consider integration requirements with other tools and systems, as ETL processes may be more agnostic.

6. Historical Practices:

• Organizations with legacy systems and traditional data warehousing practices may lean towards ETL.

Choosing between ETL and ELT

Depends on specific business needs, data characteristics, and the capabilities of the chosen data warehouse. There is no one-size-fits-all solution, and the decision often involves a careful analysis of these factors to determine the most efficient and cost-effective approach for a given scenario.

FAQs about   ETL & ELT

What is the main reason for being ELT vs ETL?

• The main reason for choosing ELT over ETL is to leverage the processing power of the data warehouse. ELT loads raw data into the warehouse first and performs transformations using its computational capabilities, making it well-suited for scenarios where the data warehouse is robust and scalable.

What are some trade offs between ETL and ELT?

• Performance:

ETL: Performance may be slower as transformations occur before loading into the data warehouse.

ELT: Offers potentially faster performance, leveraging the processing power of the data warehouse.

• Flexibility:

ETL: More flexible for complex data transformations before loading.

ELT: Greater flexibility in adapting to changes in data warehouse schema.

• Scalability:

ETL: Scaling requires additional resources in the ETL process.

ELT: Scales more efficiently as it leverages the scalability of the data warehouse.

• Data Volume:

ETL: May be more suitable for smaller datasets or when extensive pre-processing is required.

ELT: More efficient for large volumes of data that can benefit from parallel processing in the data warehouse.

What are ETL and ELT techniques?

ETL (Extract, Transform, Load) Techniques:

• Extract:

Data is extracted from source systems, databases, or applications.

• Transform:

Extracted data undergoes transformations, including cleaning, filtering, and restructuring.

• Load:

Transformed data is loaded into the target data warehouse or destination.

ELT (Extract, Load, Transform) Techniques:

• Extract:

Data is extracted from source systems, similar to ETL.

• Load:

Raw data is loaded into the data warehouse without significant transformation.

• Transform:

Transformation occurs within the data warehouse using its processing capabilities.

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