What is the difference between ETL and ELT?

Overview

Say you have data in a dozen different places, and you need it all in one spot, fully cleaned and ready for analysis. That is the core goal of data integration. To get the job done, data engineers rely on two primary data pipeline architectures: ETL (Extract, Transform, Load) and its modern alternative, ELT (Extract, Load, Transform). While both move data from source to storage, the timing of how they process that data changes everything. Let's break down how they work.

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 as it is 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 first loaded 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. 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.

So, how does ETL work? Think of a modern ETL pipeline as a factory assembly line. The system doesn't wait to gather all the raw materials before starting production. Instead, it multitasks—extracting new data while simultaneously cleaning the previous batch and loading the finished product. How fast this assembly line moves depends entirely on the business's needs, generally falling into two categories:

• Batch processing pipelines: This is the most popular method where data is extracted, transformed and loaded periodically.
• 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.

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.

These are some of the tools you could use for ETL:
Open-source tools: Apache Nifi.
Commercial ETL tools: Informatica and Microsoft SSIS

Now, for the longest time ETL was applauded for its data quality and governance capabilities ensuring data stored followed the outlined business requirements.
However, as companies grew, this 'clean first, store later' approach led to scaling inefficiencies. The pipeline became a bottleneck that frustrated data engineers with silent failures.
This is where ELT came in.

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.

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

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.