ETL and ELT

ELT (Extract, Load, and Transform) and ETL (Extract, Transform, and Load) are processes that businesses use to extract data from multiple Sources and combine them into a single database or data warehouse for analysis. Both methods use the following steps, but in different order to prepare the data for analysis:

  • Extract: This step involves extracting data from a Source, which may be a database like MySQL or MongoDB or any other application like Google Sheets, Google Drive, or Salesforce.

  • Load: In this step, the data is loaded to the Destination warehouse or database for analytical workloads.

  • Transform: This step can be used to cleanse, process, and convert the data into a format suitable for loading to the Destination and further analysis.


What is ETL?

In ETL (Extract, Transform, and Load), data is extracted from the Source system, loaded to a staging location where you can transform it into a form suitable for analysis and reporting purposes, and lastly, loaded to a Destination database or warehouse.


While ETL allows you to control the amount of data that is loaded to the Destination by loading just the transformed data, ETL is not suited for situations where a very high volume of data is needed is near real-time. The process of transforming the data before loading it to the Destination makes data availability extremely slow as the volume of data grows, and more complex transformations are required to accommodate different types of data. This has led to more businesses adopt ELT for data integration needs.

For example, traders and financial data scientists need near-real time data to offer near-real time insights to their customers. ETL would not serve this goal. ELT Pipelines are much more suitable in this case as they allow for faster loading of data to the Destination.


What is ELT?

ELT (Extract, Load, and Transform is the data integration method that makes raw data from your Source available in your Destination in near-real time, where you can transform and prepare it for downstream use. The shift to ELT in recent years is largely a result of the adoption of cloud data warehouses and data lakes which have brought the cost of data storage significantly down. ELT along with cloud data warehouses also allows you to store unstructured data and perform fast and large-scale data transformations. So, businesses do not need to worry about carefully selecting and then applying transformations on their data before loading it to the Destination to save on storage costs. ELT is now the preferred approach for obtaining data for analysis.


The Hevo Advantage

The ELT-driven Pipelines in Hevo bring you the following advantages:

  • Fast loading times: As the Pipelines move the data directly from the Source to the Destination, the loading time is very less.

  • On-demand Transformations: You can perform On-demand Transformations on the data in the Destination using the data processing power of cloud storage solutions such as Snowflake and Amazon Redshift. Hevo provides you Models and Workflows to transform the raw data in your Destination into a form suitable for your BI tool to perform analysis. You can also use Transformations to cleanse and prepare the raw data before loading.

  • Flexibility: With Hevo Transformations and Models and Workflows, you get the flexibility to either query the entire dataset, or only the data you need, as many number of times and in as many number of ways after it is loaded to the Destination.


ELT vs ETL

Refer to the table below for some key differences between ETL and ELT:

  ETL ELT
Data Transformation Raw data is transformed before being loaded to the Destination. Raw data is transformed after being loaded to the Destination.
Load Times ETL takes more time to load data to the Destination as the data is transformed first. ELT is faster as the data is loaded directly to the Destination.
Data Volume More suitable for small data sets that require very complex transformations. Ideal for larger data sets with more emphasis on getting real-time data for analysis.

Revision History

Refer to the following table for the list of key updates made to this page:

Date Release Description of Change
Mar-21-2022 NA New document.
Last updated on 21 Mar 2022