Home / Blogs / ELT: Extract, Load, and Transform – A Complete Guide

Table of Content
The Automated, No-Code Data Stack

Learn how Astera Data Stack can simplify and streamline your enterprise’s data management.

ELT: Extract, Load, and Transform – A Complete Guide

Mariam Anwar

Product Marketer

May 23rd, 2024

The emergence of digital applications and platforms has led to the prevalence of unstructured data, so much so that more than 80% of enterprise data is unstructured. Storing and analyzing this data is complex because it’s not machine-readable and must be structured for processing. Therefore, modern enterprises must reevaluate their data management practices to efficiently leverage mission-critical insights.

ELT — A Newer, More Effective Approach

For decades, organizations have used Extract, transform and load (ETL) to integrate data stored across disparate source systems. However, the increasing data volume, variety, and velocity presented by the big data age call for a different approach. Many data architects are now inclining toward extract, load, and transform (ELT), which is more suited for the modern data stack.

ELT is a modern data integration approach that has revolutionized the data management process. The blog discusses how ELT works, the evolution of ETL into ELT, why the latter has become a more popular approach, and whether the two approaches can coexist.

ELT vs. ETL: What’s the Difference?

  • ETL and ELT both involve three steps, i.e., data extraction, transformation, and loading. However, the difference between the two approaches is the order in which the data is transformed and loaded into the target system or database.
  • In traditional ETL, data is transformed in a staging area, i.e., before it is loaded to a destination, which significantly increases the load time and leads to inefficiencies.
  • In ELT, data is transformed after it is loaded, thus eliminating the underlying rigidity associated with specific data types and formats.
  • ELT is mostly used in modern data management architectures, such as data lakes and cloud-based data platforms, where the target system or database has the processing power and capabilities to handle the transformation of large amounts of data.

The Advent of Cloud Data Warehousing and Data Lakes

The rise of unconventional data sources such as IoT, social media, and satellite imagery, and the consequent increase in data volume, variety, and velocity, has accelerated cloud adoption as modern enterprises want to leverage cloud data warehouses and data lakes to effectively process and store data.

Cloud data warehouses such as Snowflake, Amazon Redshift, or Google Big Query are designed to meet modern-day data management requirements. They can easily store raw data and handle in-app transformations at scale. These warehouses are used in combination with cloud storage platforms such as Amazon S3, Azure Blob storage, and Google Cloud platform.

ELT in the Era of Cloud

Combining ELT and cloud data warehouses is the best approach to processing data. As data moves from sources to storage platforms and data warehouses, ELT ensures that its integrity remains intact. Moreover, it allows faster ingestion of unstructured data and enhances its interpretation to derive more value from it.

How ELT works

In addition, ELT makes it easier to track data lineage, which allows data analysts to understand where the data originated and trace errors back to the root cause.

ELT uniquely suits cloud data warehousing as cloud solutions can efficiently ingest data, store it safely, handle cloud-hosted transformations, and then load it into the preferred data dashboard for analytics and reporting.

Benefits of ELT


ELT offers greater flexibility compared to ELT. It allows users to store any type of information, including unstructured data, without transforming and structuring it. Moreover, users don’t need to create complex ETL processes before data ingestion.

ELT is also more flexible in terms of tailoring pipelines as per the change in the use case requirements since data transformation is the final step — unlike ETL, where any subsequent changes would require the entire data pipeline to be built from scratch.


ELT effectively deals with the congestion problem associated with high volumes of data. Unlike ETL, where data of predefined schemas can only be loaded and stored, ELT facilitates the storage of data with dynamic layouts and flexible schemas.

Since the transformation logic is pushed to the end in ELT, data can be loaded immediately and consumed in real-time, enabling faster decision-making.


ELT is a consumer-centric approach that allows business users to participate in data management. When data is directly loaded into a data warehouse, business and data analysts can directly view and manipulate raw data from the cloud system depending on use case requirements.

Since most cloud data warehouses are SQL-based, business users can efficiently run their queries without any loss of data during the transfer.

ETL and ELT- Substitutes or Complements?

While ETL and ELT are considered alternatives, these approaches aren’t mutually exclusive. While the latter solves many of the ETL’s problems, dubbing it the substitute might not be accurate. Both approaches have advantages and disadvantages, and their efficacy depends on the type of data assets and business requirements.

For example, if you need to integrate data with sensitive business information, ETL should be your preferred data integration approach as it allows you to structure, transform, manipulate, and secure data as per requirements before loading it to the target destination. On the other hand, when you are working with large volumes of data coming from multiple sources, where any slowdowns can adversely impact business performance, you should choose ELT.

Therefore, it’s safe to say that ELT and ETL can coexist and are vital for organizational success. Instead of seeing the two as substitutes, you should view them as counterparts to leverage business intelligence essential for growth.

Features to Look Out For in ELT Tools

It is important to choose the right ELT tool for your organization. Here are some features that you need to keep in mind before you invest in one:

  1. Code-Free Architecture

An ELT tool should provide the same level of usability to both developers and business users without the need for advance technical knowledge. A no-code ELT tool reduces dependence on the IT team and provides ease of use and accessibility to information, thereby enabling enterprises to leverage valuable insights quickly and efficiently.

  1. Automation

A typical enterprise processes high data volumes daily. Performing similar tasks repeatedly wastes time, resources, and effort. An ELT tool should have automation and orchestration capabilities so that you can schedule integration and transformation jobs easily, be it a simple data flow or a complex workflow.

  1. Connectivity to Multiple Data Sources

ELT jobs become easier when a tool offers native connectivity to various sources and destinations. Before purchasing a tool, look at the library of connectors it supports. Ideally, an ELT tool should have native connectivity to popular cloud data bases and storage platforms such as Amazon S3, Azure Blob, Snowflake, and Amazon Redshift amongst others.

Building ELT Pipelines with Astera Centerprise

Astera Centerprise is a code-free data integration tool with a powerful ETL/ELT engine. With Astera’s ELT or pushdown optimization mode, you can push down the transformation logic to the source or target database when they reside on the same server. Here is why Astera Centerprise is the perfect ELT solution:

  1. Support for various cloud platforms, such as Snowflake, Redshift, Amazon S3, and Blob storage, making it a perfect fit for cloud environments.
  2. Two pushdown modes: partial and full pushdown. Astera’s intelligent algorithm decides which of the two best suits a job’s performance.
  3. The ELT mode executes automatically generated SQL queries on the destination.
  4. Native SQL support for transformations, including join, aggregate, union, route, switch, various types of lookups, and database writing strategies.

Astera’s ELT functionalities, coupled with powerful workflow automation and orchestration capabilities, accelerate data integration of large volumes of data while minimizing latency. With Astera Centerprise, you can harness the power of ELT and optimize the performance of even the most complex data flows.

You can download Astera Centerprise’s 14-day free trial today to integrate enormous volumes of data at incredible speeds.

What Is Database Schema? A Comprehensive Guide
ANSI X12 vs EDIFACT: Key Differences
What Is Metadata and Why Is It Important?
Considering Astera For Your Data Management Needs?

Establish code-free connectivity with your enterprise applications, databases, and cloud applications to integrate all your data.

Let’s Connect Now!