ETL vs ELT

The letters stand for Extract, Transform, and Load

• Extract refers to the process of pulling data from a source such as an SQL or NoSQL database, an XML file or a cloud platform.

• Transform refers to the process of converting the format or structure of a data set to match that of a target system.

• Load refers to the process of placing a data set into a target system.

The difference between ETL and ELT is when data transformation happens.
Here is a side-by-side comparison of the two processes:

ETL stands for Extract > Transform > Load
In the ETL process, data transformation is performed in a staging area outside of the data warehouse and the entire data must be transformed before loading. As a result, transforming larger data sets can take a long time up front but analysis can take place immediately once the ETL process is complete.

ELT stands for Extract > Load > Transform
In the ELT process, data transformation is performed on an as-needed basis in the target system itself. As a result, the transformation step takes little time but can slow down the querying and analysis processes if there is not sufficient processing power in the cloud solution.

There is a place for each process.
The ETL process is appropriate for small data sets which require complex transformations. The ELT process is more appropriate for larger data sets and when timeliness is important.

ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of the traditional data pipeline. The ETL process is appropriate for small data sets which require complex transformations.

ETL Process

1. A predetermined subset of data is extracted from the source.

2. Data is transformed in a staging area in some way such as data mapping, applying concatenations or calculations. Transforming the data before it is loaded is necessary to deal with the constraints of traditional data warehouses.

3. Data is loaded into the target data warehouse system and is ready to be analyzed by BI or data analytics tools.

Key benefits of ETL

• Data analysis on a single, pre-defined use case can be more stable and faster with the ETL process given that the data set has already been structured and transformed.

• Compliance with GDPR, HIPAA, and CCPA standards is easier with ETL given that users can omit any sensitive data prior to loading in the target system.

Learn More About the ETL Process

ELT is an acronym for “Extract, Load, and Transform” and describes the three stages of the modern data pipeline. The ELT process is more cost effective then ETL, is appropriate for larger data sets and when timeliness is important.

ELT Process

1. All data is extracted from the source.

2. All data is immediately loaded into the target system (either a data warehouse, data mart, or data lake). This can include raw, semi-structured and structured data types.

3. Data is transformed in the target system and is ready to be analyzed by BI tools or data analytics tools

Typically, the target system for ELT is a cloud-based data lake, data mart, data warehouse or data lakehouse. These cloud-based platforms such as Amazon Redshift, Snowflake, Azure Synapse, Databricks and Amazon EMR offer near-unlimited storage and extensive processing power. This allows users to extract and load any and all data they may need in near real time. The cloud platforms transform the data for any BI, analytics, or predictive modeling use case at any time.

Key benefits of ELT

• Real-time, flexible data analysis. Users have the flexibility to explore the complete data set, including real-time data, in any direction, without having to wait for IT to extract, transform and load more data.

• Lower cost and lower maintenance. ELT benefits from a robust ecosystem of cloud-based platforms which offer much lower costs and a variety of plan options to store and process data. And, the ELT process typically requires low maintenance given that all data is always available and the transformation process is usually automated and cloud-based.

Learn More About the ELT Process

Many organizations use both the ETL and ELT processes to cover the spectrum of their data pipeline needs. Let’s take a side-by-side look at the pros and cons of ETL vs ELT, and how they can work in tandem to provide a holistic data integration solution for your business.

ETL vs ELT — 10 Key Differences: