What is the difference between ETL and ELT?

Cliffe Okoth — Fri, 10 Apr 2026 23:21:50 +0000

Overview

In the data engineering ecosystem, various data integration processes are used to make sense of data. In this article, we'll focus on the two popular approaches: ETL and ELT. But first, a few definitions:

Data Ingestion: This is the process of moving data from source systems into storage or simply, data movement from point A to point B.
Data Integration: This process combines data from multiple source into a coherent format.
Data Pipeline: This is any combination of systems and processes that move data through the stages of the data engineering lifecycle e.g. ETL Pipeline (extracts raw data from an API using a Python script -> transforms data with dbt -> loads transformed data into a storage database)
Data lakes: These are special kinds of data stores that accept any kind of structured or unstructured data without transformation.

Now, what exactly is ETL?

ETL

ETL(Extract, Transform, Load) is a data integration process that extracts raw data from a single or multiple sources, transforms this data into a usable format, then loads the resultant data into a database where end-users can access it.
What do these three processes entail?

Extract: This is the first step of the process. It includes extracting data from target sources that can range from structured sources like databases (SQL, NoSQL), to semi-structured data (JSON, XML) to unstructured data (emails, flat files). It is crucial in this step, to gather data without altering its original format, enabling it to be further processed in the next stage.
** Transform:** In this step, data gets cleansed and restructured, to meet operational needs. Data is usually not loaded directly into the data destination, it is commonly uploaded into a staging database (layer between the raw data and the clean data). This ensures a quick roll back in case something goes wrong in the pipeline. During this stage, you have the possibility to generate audit reports for regulatory compliance or diagnose and repair any data issues. Common transformations include:
- Data Filtering: Removing irrelevant data.
- Data Sorting: Organizing data into a required order.
- Data Aggregating: Summarizing data to provide meaningful insights (e.g. average sales, total sales).
Load: This is the final process where transformed data is uploaded to a target database where end-users can access it. Depending on the use case, there are two types of loading methods:
- Full Load: All data is loaded into the target system, often used during the initial population of the warehouse.
- Incremental Load: Only new or updated data is loaded, making this method more efficient for ongoing data updates.

These ETL processes occur via a mechanism known as an ETL Pipeline. This is basically a data pipeline for ETL. This pipeline ensures that instead of completing each step sequentially, data is extracted, transformed and loaded concurrently. What does this mean? While data is being extracted, it is transformed, and as it is being transformed, it is being loaded into the warehouse. Therefore, new data can continue being extracted and processed thus enhancing efficiency and speed. ETL pipelines are categorized based on their latency. The most common ones use either batch or real-time processing:

Batch processing pipelines: This is the most popular method where data is extracted, transformed and loaded periodically. These ETL processes are scheduled to occur at a certain time on a certain day e.g. traditional analytics
Real-time processing pipelines: This method depends on streaming sources for data, with transformations performed using a real-time processing engine like Spark. Unlike batch processing which is scheduled, this method occurs in real time e.g fraud detection.

Advantages of ETL

Data quality: Data quality and consistency are often improved in ETL processes through cleansing and transformation steps.
Data governance: ETL can help enforce data governance policies by ensuring that data is transformed and loaded into the target system in a consistent and compliant manner.
Legacy systems: ETL is often used to integrate data from legacy systems that may not be compatible with modern data architectures.
Complex transformations: ETL tools often provide a wide range of transformation capabilities, making them suitable for complex data manipulation tasks.
Enhanced Decision-Making: ETL helps businesses derive actionable insights, enabling better forecasting, resource allocation and strategic planning.
Operational Efficiency: Automating the data pipeline through ETL speeds up data processing, allowing organizations to make real-time decisions based on the most current data.

Challenges of ETL

While ETL is essential, building and maintaining reliable data pipelines has become one of the more challenging parts of data engineering. These are some of the issues that plague this process:

Limited Re-usability: A pipeline built in one environment cannot be used in another, even if the underlying code is very similar, meaning data engineers are often the bottleneck and tasked with reinventing the wheel every time.
Data Quality: Managing data quality in increasingly complex pipeline architectures is difficult. Bad data is often allowed to flow through a pipeline undetected, devaluing the entire data set. To maintain quality and ensure reliable insights, data engineers are required to write extensive custom code to implement quality checks and validation at every step of the pipeline.
Scaling Inefficiencies: As pipelines grow in scale and complexity, companies face increased operational load managing them, which makes data reliability incredibly difficult to maintain. Data processing infrastructure has to be set up, scaled, restarted, patched and updated - which translates to increased time and cost.
Silent Failures: Pipeline failures are difficult to identify and even more difficult to solve due to lack of visibility and tooling.

Regardless of these challenges, ETL is a crucial process for data-driven businesses.

Solutions to Overcome ETL Challenges

Data Quality Management: Use data validation and cleansing tools, along with automated checks, to ensure accurate and relevant data during the ETL process.
Optimization Techniques: Overcome performance bottlenecks by making tasks parallel, using batch processing and leveraging cloud solutions for better processing power and storage.
Scalable ETL Systems: Modern cloud-based ETL tools offer scalability, automation and efficient handling of growing data volumes.

Real world use cases

These are some of the ways ETL is used in the real world:

Sensor Data Integration: Gathering raw, continuous data from multiple IoT sensors, filtering out anomalies, and moving the clean data to a single point where it can be analyzed for equipment maintenance.
Cloud Migration: Moving legacy data from an on-premise (client-managed) warehouse, transforming its structure to match modern schemas, and loading it into the new cloud platform.
Marketing Data Integration: Collecting campaign data from various distinct sources (like Facebook Ads, Google Ads, and email platforms), standardizing currency and date formats and preparing it for analysis before loading it into a final reporting destination.
Database Replication: Continuously extracting data from multiple operational databases, transforming it to unified schema and replicating it into a central data warehouse for reporting.

What are the tools you can use for ETL?
ETL tools are categorized as: Open-source(or free) and Commercial (paid)
Open-source tools offer flexibility and are ideal for small businesses or individuals venturing into the data space. They are free and can be modified to individual standards. They include Apache Nifi.
On the other hand, Commercial ETL tools are easier to use and are more scalable. They cater to large organizations which have more data that require high performance, minimal failure, better customer support, security and advanced functionality. They however come with licensing costs. Good examples are Informatica and Microsoft SSIS

ELT

ELT stands for "Extract, Load, Transform." In this process, the transformation of data occurs after it is loaded into storage. That means there's no need for data staging.

The ELT process does not differ much from ETL, transformation just comes after data loading. It is broken up as follows:

Extract: This initial step involves collecting raw data.
Load: In the second step, instead of being transformed in a separate staging area(e.g. ETL tool environment), the extracted raw data is loaded, often in its original format or with minimal processing, directly into a data lake.
Transform: The final step occurs after the data is safely housed in the target system. Using the computational power of the data warehouse or data lake, the raw data is cleaned, structured and converted into a suitable format for end users.

Key Advantages of ELT

The ELT approach offers several potential advantages, particularly in environments dealing with large data volumes and diverse data types:

Flexibility: The ELT process allows you to store new, unstructured data with ease, without transformation.
Speed: Cloud warehouses enable quick data transformation due to their processing power.
Cost efficiency: Using the computing power of a cloud data warehouse for transformations can sometimes be more cost-effective than maintaining separate infrastructure, especially when the data warehouse offers optimized processing.

Challenges of ELT

ELT also comes with a fair amount of challenges:

Data governance and security: Loading raw data, which might contain sensitive user information, into a data lake or data warehouse requires robust data governance and compliance measures.
Data Management: If raw data loaded into a data lake isn't properly catalogued, the data lake can turn into a "data swamp" where data is hard to find, trust or use effectively. A strong data management strategy is crucial.
Data Quality: Since transformations occur later in the process, ensuring data quality might require dedicated steps post-loading. Monitoring and validating data within the target system becomes important.

Real world use cases

This is how ELT can be used in the real world:

Mobile Lending Applications: Ingesting massive volumes of raw, unstructured user and transaction data from a mobile lending app directly into a data lake then using the warehouse's computing power to transform specific segments of that data to train machine learning algorithms for credit scoring.
Event Analytics: Dumping massive volumes of raw website clickstream data or server logs directly into a cloud data warehouse as soon as they are generated. Transformations are only applied later when data analysts need to query specific user behaviors or run a security audit.
Rapid Storing of Unstructured Data: Loading new, completely unstructured data (like raw text, audio files, or social media feeds) directly into storage, providing immediate access to all raw information whenever it is needed for future analysis.

ELT Tools

Open-source tools
* ELT Platforms: Airbyte
* Orchestrators: Apache Airflow
* Transformation Framework: data build tool (dbt)

Commercial tools
* ELT Platforms: Matillion, Hevo Data, Weld
* Connectors: Fivetran
* Data Replication: Stitch

ETL vs. ELT

While ETL and ELT serve as data integration methods, their distinction lies in the timing of data transformation.

The choice between ETL and ELT depends on several factors, such as:

Data complexity: ETL is often used for complex transformations that require specialized tools and expertise.
Skills and resources: ETL requires specialized skills and resources for building and maintaining transformation pipelines. ELT may be easier to implement because it leverages the resources of cloud data warehouses.
Data volume: ELT is generally better suited for large volumes of data because it leverages the processing power of cloud data warehouses for transformations.
Target system: ELT is best suited for cloud-based data warehouses and data lakes that have the processing power to handle transformations.

Summary

To cap this off, in modern data engineering, transforming raw data into actionable insights requires robust data integration pipelines. The two dominant approaches for moving and preparing this data are ETL and ELT.

ETL (Extract, Transform, Load): This traditional approach extracts raw data, cleans and structures it within an intermediate staging area, and finally loads it into a target database or data warehouse.
- Best for: Enforcing strict data quality, ensuring regulatory compliance/governance, and executing highly complex transformations—often used with legacy systems.
- Trade-offs: Can suffer from scaling inefficiencies, rigid maintenance requirements, and processing bottlenecks.
ELT (Extract, Load, Transform): This modern approach extracts raw data and loads it directly into a data lake or cloud data warehouse without prior staging. Transformations are performed post-load, leveraging the massive computational power of the destination system.
- Best for: Handling massive data volumes, quickly ingesting unstructured data and minimizing latency.
- Trade-offs: Requires robust security measures to protect sensitive raw data and strict cataloging to prevent the data lake from degrading into an unmanageable mess.

In conclusion, the choice between the two processes depends heavily on one's specific needs. ETL remains the standard for complex transformations where data quality must be guaranteed prior to storage. Conversely, ELT has emerged as the preferred choice for modern, cloud-based environments dealing with massive, diverse datasets where speed and flexibility are the top priorities.

Forem: Cliffe Okoth

What is the difference between ETL and ELT?

Overview

ETL

Advantages of ETL

Challenges of ETL

Solutions to Overcome ETL Challenges

Real world use cases

ELT

Key Advantages of ELT

Challenges of ELT

Real world use cases

ELT Tools

ETL vs. ELT

Summary