ETL vs ELT: Difference, Examples, Pros and Cons, When to Choose ETL or ELT

With the help of ELT (Extract, Load, Transform) processes, it became possible to process large amounts of data much faster. This modern approach differs from the ETL (Extract, Transform, Load) method and offers specific advantages in certain cases.

What is ETL?

ETL is an acronym for the three stages of Extract, Transform, and Load.

ETL Process

• Extract: Information is gathered from source systems.
• Transform: The extracted data is formatted and structured for analysis and processing.
• Load: The transformed data is stored in a data warehouse or any other data storage system.

Pros of ETL

• Data Quality: Cleans and enhances the quality of data before loading.
• Control: Offers greater control over data transformations.
• Compliance: Supports compliance with the data governance regulations.

Cons of ETL

• Time-Consuming: The transformation process can be lengthy. 
• Resource-Intensive: The transformation step requires a significant amount of resources.
• Scalability Issues: May struggle to handle large data sets.

ETL Example

ETL is typically applied in traditional data warehousing frameworks, where data collected from different sources is cleansed, transformed, and then loaded into a data warehouse for analysis. For example, financial institutions apply ETL mechanisms to gather transactional data from various systems put it into the data warehouse for reporting.

What is ELT?

ELT is an acronym that is an abbreviation for three processes, namely Extract, Load, and Transform.

ELT Process

• Extract: Information is gathered from different source systems.
• Load: The extracted data is then transferred to the target system, which is mostly a data lake or cloud storage.
• Transform: The target system transforms the computation capacity of the data.

Pros of ELT

• Speed: Reduces calls to the data source since the data is loaded directly without prior manipulation.
• Scalability: This is more appropriate for large datasets due to the application of powerful cloud storage and computing.
• Flexibility: It makes it possible to perform individual transformations and analytics as and when required.

Cons of ELT

• Complexity: The management of changes within the target system may not be easy.
• Data Quality: There is a possibility of having lower-quality data if the transformations are not handled properly.
• Compliance Risks: There may be difficulties in matters of data compliance and governance.

ELT Example

The transformation that ELT offers is particularly well-suited to large-scale data architectures such as distributed data lakes, where the cloud’s computational capabilities can be utilised. For instance, firms employing Amazon Redshift or Google BigQuery transfer and load big data and transform the data as required for analysis.

Key Differences Between ETL and ELT

Here are some key differences between ETL and ELT:

Comparison Between ELT and ETL

ETL and ELT are the two processes of data integration that are used for preparing data for analysis. They involve three primary stages: the process of obtaining data from different sources, cleaning the data to make it understandable for operational use, and then uploading this data into a data warehouse. Although the given stages are not presented in the same order in both approaches, their main goal is to deliver clean, organised, and usable data.

When to Use ELT vs ETL

When to use ETL:

• When using conventional data warehouses.
• If data transformation has to happen before loading because of compliance issues or due to data quality issues.
• This is for situations where some sort of transformation is needed before data can be used.

When to use ELT:

• For big data or when using cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, or Snowflake. It is particularly useful for use cases requiring transformations on-demand and in real-time.
• In cases where there is a mass amount of data that is required to be loaded at once.
• Under conditions where the transformation logic can be effectively managed within the data warehouse.

Best Practices for Implementing ETL and ELT

Some of the best practices for implementing ETL and ELT include:

• Data Quality: Adopt high-quality data and try to maintain the data quality and consistency from the initial stage to the final stage.
• Scalability: Select solutions that are capable of expanding as the volume of data increases.
• Automation: Introduce automation to minimise on-task redundancy and increase the accuracy of the results obtained.
• Security: There should be strong data security measures, particularly when extracting and loading data.
• Monitoring and Maintenance: Conduct data process checkups and ensure that data pipelines are functioning correctly.

It is essential to know that when it comes to choosing between ETL and ELT, the choice is relative to the organisation’s needs. ELT is suitable for use in large-scale data processing, especially in the cloud, while ETL is more suitable in environments that need more data quality and control. This knowledge will assist you in making the right decision and understanding the strengths and weaknesses of each strategy.