Database Management

Choosing Your Data Dance: ETL vs. ELT

Malkanagouda Patil

Malkanagouda Patil

Dec 25th - 1 min read


ETL stands for Extract, Transform, Load, and it refers to a process in data integration and data warehousing. Each step in the ETL process plays a crucial role in moving and preparing data for analysis.

In this phase, data is gathered from various sources, such as databases, flat files, APIs, or other systems. The goal is to pull the relevant data from these sources for further processing.

Once the data is extracted, it undergoes a transformation process. This involves cleaning, structuring, and converting the data into a format that is suitable for analysis and reporting. Transformation may include filtering out unnecessary information, handling missing data, and converting data types.

After the data has been extracted and transformed, it is loaded into a target database or data warehouse. The destination could be a relational database, a data lake, a data mart, or any other storage system designed to support reporting and analysis.


ELT stands for Extract, Load, Transform, which is an alternative approach to data integration and processing compared to ETL (Extract, Transform, Load). In ELT, the transformation of data occurs after the data has been loaded into the target system, typically a data warehouse.

Data is extracted from source systems, such as databases, applications, or other data repositories. The raw data is then moved to the target system without significant transformation.

The extracted data is loaded into a target system, usually a data warehouse or a big data platform. This process involves moving large volumes of raw data into a storage environment where it can be efficiently managed and queried.

After the data is loaded into the target system, transformations, and processing are applied within the target environment. This can involve cleaning, structuring, and organizing the data to make it suitable for analysis. Transformations may include aggregations, joins, and other data manipulations.

Difference Between ETL and ELT

Category ETL Process ELT Process
Sequence of Operation Extract, Transform, Load Extract, Load, Transform
Transformation Location Occurs in a separate staging area or dedicated server Occurs within the target system
Target System Data warehouse or storage system optimized for analysis Data warehouse or storage system optimized for analysis
Processing power Transformation occurs before loading, often suitable for source systems with limited processing power Leverages the processing power of the target system
Speed ETL is Slower than ELT ELT is faster than ETL as it can use the internal resources of the data warehouse
Data compatibility Best with structured Data It can handle structured, semi-structured, and unstructured data

Unveiling Variances: ETL vs. ELT Processes Explained

ETL Process:

Extract: Get data from different sources.

Transform: Change and organize the data using another server.

Load: Put the transformed data into a destination database.

In ETL, data gets transformed before moving it to the target database.

ELT Process:

Extract: Take data from various sources.

Load: Put the data as it is into a data warehouse or data lake.

Transform: Make necessary changes to the data inside the target system.

With ELT, all the cleaning and changes to the data happen within the data warehouse. You can work with and modify the raw data multiple times.


  • ETL: Been around since the 1970s, widely used with data warehouses. But, it needed custom processes for each data source.
  • ELT: Became popular with cloud technologies. Companies can store a lot of raw data and analyze it later. ELT is the modern way for effective analytics.


In the data dance, picking ETL or ELT is like choosing dance steps. ETL transforms data before putting it away, while ELT loads raw data and transforms it later. ELT is quicker, uses the warehouse's resources, and can handle different data types. So, choose your data dance wisely for smooth insights and business goals.

about the author

Malkanagouda Patil is a data enthusiast and a content researcher. He works as a business analyst who works predominantly on deriving insights and intelligence using SQL, Power BI & Python Programming