ETL vs ELT: Which One Should You Use and Why?

Introduction

In today’s data-driven world, organizations rely heavily on data to make decisions and build products. However, raw data is rarely useful in its original form. Raw data often needs to be collected, cleaned, and structured before it can be used to generate insights. This is where data pipelines come in.

A Data Pipeline is an automated process that moves data from source to destination. There are two approaches in data pipelines:

• ETL (Extract, Transform, Load)
• ELT (Extract, Load, Transform)

These two may look similar, but the difference shows in their execution order. It significantly impacts system design, scalability, cost, and performance.

ETL

ETL stands for Extract, Transform, Load. The general idea behind it is to clean and shape your data before you store it.
Extract: This involves collecting data from various sources such as:

• APIs
• Databases
• IoT sensors
• Files (CSV, JSON) Data at this stage is raw and unprocessed

Transform: The raw data is processed here. Some processing steps incude:

• Cleaning (removing duplicates, handling missing values)
• Formatting (dates, currencies)
• Aggregating (daily totals, averages)
• Joining multiple datasets The data at this stage is not saved permanently yet.

Load: After transformation, the cleaned, structured data is then written into the destination/target systems which include:

• Data warehouse
• Data lake
• Database

ETL Workflow Diagram

In ETL, only clean and structured data is loaded into the warehouse.

Example: Retail Sales System (ETL)

Imagine you are working on an e-commerce project.

Data Sources:

• Orders database
• Payment system
• Customer database

ETL Process

Step 1: Extract

• Pull raw data from all systems

Step 2: Transform

• Remove duplicate orders
• Convert currencies (USD to KES)
• Standardize timestamps
• Join customer data with orders

Step 3: Load

Store clean tables in a warehouse, e.g.:

• sales_summary
• customer_metrics

Advantages of ETL

• Clean data is stored - high data quality
• Better for compliance - sensitive data is filtered early
• Reduced storage usage - only useful data is kept

Disadvantages of ETL

• Slower for large datasets
• Less flexible - you could lose raw data
• Harder to reprocess data later

Common ETL Tools

• Apache Airflow
• Talend
• Informatica PowerCenter
• Microsoft SQL Server Integration Services (SSIS)

ELT

ELT stands for Extract, Load, Transform.It is a slight reversal to the ETL process, i.e:

1. Extract data
2. Load raw data into a storage system
3. Transform data inside the storage system.

It is a newer pattern, made practical by the rise of cloud data warehouses such as BigQuery, Redshift and Snowflake.

ELT Workflow Diagram

Example: Streaming Platform (ELT)

Imagine a platform like a video streaming service.

Data Sources:

• User activity logs
• Search queries
• Watch history

ELT Process:

Step 1: Extract

• Collect raw logs from applications

Step 2: Load

Store everything in a cloud warehouse like:

• Google BigQuery
• Snowflake

Step 3: Transform

Use SQL or dbt (Data Build Tool) to:

• Create user engagement metrics
• Build recommendation features
• Aggregate watch time

Advantages of ELT

• Faster data ingestion
• Highly scalable - great for big data
• Flexible - raw data is always available
• Ideal for machine learning workflows

Disadvantages of ELT

• Higher storage costs
• Requires powerful data warehouses
• Data quality issues may exist in raw data

Common ELT Tools

• Snowflake
• Google BigQuery
• Amazon Redshift
• Databricks
• dbt (Data Build Tool)

Key Differences Between ETL and ELT

Feature	ETL	ELT
Order	Extract → Transform → Load	Extract → Load → Transform
Transformation Location	Before storage	After storage
Data Stored	Processed only	Raw + processed
Speed	Slower	Faster
Flexibility	Low	High
Best Use Case	Structured data	Big data & analytics

Real World Use Cases

ETL

ETL remains the right choice in several important scenarios:

• Highly Regulated Industries such as Banks, Hospitals, and Insurance companies. These industries contain data with sensitive fields which should be masked or removed in the transform stage before reaching the warehouse.
• Low storage environments. When warehouse storage is expensive or limited, loading only cleaned, aggregated data keeps costs down.
• Legacy on-premise systems. Many enterprises run warehouse platforms like IBM Db2, Teradata, or SQL Server. These systems weren't designed to ingest raw data dumps or run transformation logic at scale. ETL processes pre-shape data into tight, efficient schemas before loading.

ELT

• Cloud data warehouses. When using data warehouses such as BigQuery, Snowflake, or Redshift, you have enormous compute power available on-demand. Running SQL transformations inside the warehouse is faster and cheaper than maintaining a separate ETL server.
• Real-time and near-real-time analytics, e.g in IoT systems. Streaming tools like Kafka can push events directly into a warehouse. The transform step allows continous processing, updating dashboards continuously.
• Data exploration and discovery. When a new data source is connected, you often don't yet know which fields you'll need or what transformations make sense. ELT lets you land the raw data first and figure out the transformation logic incrementally, without having to re-extract.

How to choose between ETL and ELT

Use ETL if:

• You need strict data validation
• You handle sensitive data
• Storage is limited
• You use on-prem systems

Use ELT if:

• You are working with big data
• You use cloud platforms
• You need flexibility
• You are building ML systems

Conclusion

Both ETL and ELT are essential in data engineering. While ETL is used in environments with strict data requirements, ELT is best for scalable, flexible, modern data systems. The main difference is when Transformation happens.