ETL vs ELT: Must Know Differences - Data Warehousing Data Warehousing Reading Time: 5 minutes

What is ETL?

ETL stands for Extract, Transform & Load, and ELT stands for Extract, Load & Transform, and so in ETL Transforming the data into a common format is done before loading.

we will cover these topics: hide 1) What is ELT? 2) Difference between ETL vs. ELT 3) Summary: 3.1) What to read next?

What is ELT?

In ELT, loading the data to the destination is performed first, and then the transformation is applied based on the destination format. The data first copied to the target and then transformed in place. In some architectures, the Staging area using ELT system is a part of the target. In most architectures, ELT systems directly transform data in the target database.

ELT is preferred to be used with no-Sql databases like Hadoop cluster, data appliance or cloud installation.

Difference between ETL vs. ELT

ETL and ELT process are different in following parameters:

Parameters	ETL	ELT
Process	Data is transformed at staging server and then transferred to Datawarehouse DB.	Data remains in the DB of the Datawarehouse.
Code Usage	Used for Compute-intensive Transformations Small amount of data	Used for High amounts of data
Transformation	Transformations are done in ETL server/staging area.	Transformations are performed in the target system
Time-Load	Data first loaded into staging and later loaded into target system. Time intensive.	Here since data is directly loaded into the target systems initially and all transformations are carried out at the objective systems.
Time-Transformation	Since this process involves loading the data into ETL systems first and then into the respective target system this pulls in a comparatively larger time.	In ELT process, speed is never dependent on the size of the data.
Time- Maintenance	It needs highs maintenance as you need to select data to load and transform.	Low maintenance as data is always available.
Implementation Complexity	At an early stage, easier to implement.	To implement ELT process organization should have deep knowledge of tools and expert skills.
Support for Data warehouse	ETL model used for on-premises, relational and structured data.	Used in scalable cloud infrastructure which supports structured, unstructured data sources.
Data Lake Support	Does not support.	Unstructured data can be processed with data lakes here.
Complexity	The ETL process loads only the important data, as identified at design time.	This process involves development from the output-backward and loading only relevant data.
Cost	High costs for small and medium businesses.	Low entry costs using online Software as a Service Platforms.
Lookups	In the ETL process, both facts and dimensions need to be available in staging area.	All data will be available because Extract and load occur in one single action.
Aggregations	Complexity increase with the additional amount of data in the dataset.	Power of the target platform can process significant amount of data quickly.
Calculations	Overwrites existing column or Need to append the dataset and push to the target platform.	Easily add the calculated column to the existing table.
Maturity	The process is used for over two decades. It is well documented and best practices easily available.	Relatively new concept and complex to implement.
Hardware	Most tools have unique hardware requirements that are expensive.	Being Saas hardware cost is not an issue.
Support for Unstructured Data	Mostly supports relational data	Support for unstructured data readily available.

Summary:

Criteria	ETL	ELT
Flexibility	High	Low
Working methodology	Data from the source system to the data warehouse	Leverages the target system to transform data
Performance	Average	Good

ETL stands for Extract, Transform and Load while ELT stands for Extract, Load, Transform
In ETL process data flows from the source to staging to the target. ELT lets the target system to do the transformation. No staging system involved.
ETL is an older concept and been there in the market for more than two decades, ELT relatively new concept and comparatively complex to get implemented.
In an ETL case, a large number of tools have only one of its kind hardware requirements that are posh. In the case of an ELT Since this falls under Saas hardware cost is not a concern.
To carry out a lookup, ETL operates row by row pattern to map a fact-value with its dimension key element from a different table. In ELT we can directly map fact-value with dimension key elements.
In ETL Relational data is prioritized here, whereas ELT Readily supports unstructured data.
ELT address many a challenge of ELT but is expensive and requires niche skills to implement and maintain.

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