ETL vs ELT: Choosing the Right Approach for Your Data Pipeline

ETL vs ELT

As data becomes increasingly important for businesses, it’s crucial to have an efficient data pipeline that can extract, transform, and load data from multiple sources into a centralized location.

If you are working with data, you have probably heard of ETL and ELT. These are two common methods of data integration that involve moving data from one or more sources to a destination system, such as a data warehouse or a data lake.

What is ETL?

ETL — Extract, Transform, and Load

ETL stands for Extract, Transform, and Load. It is a traditional data integration method that follows these steps:

• Extract: Data is extracted from one or more source systems, such as databases, files, APIs, etc.
• Transform: Data is transformed into a common format and structure that is compatible with the destination system. This may involve cleaning, filtering, aggregating, validating, enriching, or applying business logic to the data
• Load: Data is loaded into the destination system, such as a data warehouse or a data lake

Advantages

• after using ETL to structure and transform data, data queries become more efficient, leading to faster analysis
• can be used in both on-premise and cloud-based environments, allowing organizations to take data from on-premise systems and load it to a cloud datastore
• can help companies comply with data privacy regulations such as GDPR by transforming data before it reaches its destination, allowing them to remove, mask, or encrypt sensitive data
• has been in practice for over two decades, resulting in more engineers with experience in ETL implementations and more ETL tools in the marketplace to build data pipelines within organizations

Disadvantages

• can be time-consuming and resource-intensive, as it requires a lot of processing power and storage space to transform large volumes of data before loading them
• can be complex and difficult to maintain, as it involves writing and debugging custom code or scripts for each data source and transformation logic
• can be inflexible and rigid, as it requires predefined schemas and rules for the data. Any changes in the source or destination systems may require modifying the ETL code or pipeline
• can result in data loss or degradation, as some data may be discarded or altered during the transformation process

Use cases

ETL is typically used for structured or semi-structured data that needs to be processed before being stored in a relational data warehouse. ETL can ensure data quality, consistency, and accuracy by applying various transformations and validations to the data.

What is ELT?

ELT — Extract, Load, and Transform

ELT stands for Extract, Load, and Transform. It is a newer data integration method that follows these steps:

• Extract: Data is extracted from one or more source systems, just like in ETL
• Load: Data is loaded into the destination system without any transformation. The destination system is usually a cloud-based data warehouse or a data lake that can handle large volumes of raw and unstructured data
• Transform: Data is transformed within the destination system using its native processing capabilities. This may involve SQL queries, analytical functions, machine learning models, etc.

Advantages

• can be faster and more efficient, as it leverages the processing power and scalability of the cloud-based destination system to transform the data on-demand
• can be simpler and easier to manage, as it reduces the need for custom code or scripts for each data source and transformation logic
• can be more flexible and agile, as it allows for schema-on-read rather than schema-on-write. This means that the data can be transformed according to different needs and use cases without affecting the original data
• can preserve data integrity and completeness, as all the raw data is loaded into the destination system without any modification or deletion

Disadvantages

• can be more expensive, as it requires more storage space and computing resources to store and process large volumes of raw data in the cloud
• can be less secure and compliant, as it exposes sensitive or personal data to potential breaches or violations during the loading process
• can be less reliable and consistent, as it depends on the quality and availability of the destination system to transform the data correctly

Use cases

ELT is typically used for unstructured or raw data that needs to be stored in a scalable and flexible data warehouse or a data lake. ELT can enable faster and easier data ingestion by skipping the transformation step before loading.

How to Choose?

There is no one-size-fits-all solution for data integration. The choice between ETL and ELT depends on various factors:

• type and volume of your data sources
• structure and format of your destination system
• purpose and frequency of your data analysis
• budget and resources of your data project
• skills and preferences of your data team

In order to understand what best fits to your needs let’s take a look at how they differ from each other.

Comparison

Conclusion

ETL and ELT are two common methods of data integration that involve moving data from one or more sources to a destination system. The main difference between ETL and ELT is the order of operations between the two processes that make them uniquely suited for different situations.