Data is the new gold—but only if you know how to manage and process it properly. Whether it’s for ensuring data quality, running real-time analytics, building business intelligence dashboards, or creating forecasting models, a reliable data integration strategy is essential.
But how do you move data from multiple sources into your data warehouse or data lake efficiently? The answer lies in two powerful methods: ETL and ELT.
This article discusses how ETL vs ELT work, their key differences, and when you should choose which method for your data integration needs.
What Are ELT and ETL?
ETL and ELT are core processes in data engineering that move data from source systems to a central target system like a data warehouse or data lake.
ETL extracts, transforms, then loads structured data into a target system, ensuring it’s cleaned and consistent for analysis.
ELT extracts and loads raw data first, then transforms it as needed—perfect for handling large volumes of dataand modern cloud-based data warehouses.
Side Note: Data Warehouses and Data Lakes
A data warehouse stores structured, cleaned data ready for analytics and business intelligence.
A data lake holds raw data in its original unstructured or semi-structured form, making it flexible for different data types and use cases.
How Does the ETL Process Work?
1. Extract.
Data is pulled from multiple data sources—like SQL databases, APIs, flat files (CSV/Excel), ERP or CRM systems.
2. Transform.
The transformation process cleans and formats raw data into a consistent structure. It includes:
- Data type conversions (e.g., text to date)
- Calculations (e.g., sales = quantity × price)
- Data cleansing (removing duplicates, filling gaps)
- Standardisation (consistent formats, country codes)·
- Joins (combining data sources)
- Validation (ensuring accuracy)
ETL tools and scripts automate these steps, ensuring high data quality.
3. Load.
The processed, clean data is loaded into the target data warehouse or data lake. This can be:
- Full load (replacing all data)
- Incremental load (only new/changed records)
How Does the ELT Workflow Work?
ELT reverses the traditional ETL pipeline:
- Extract data from multiple sources.
- Load it directly into the data warehouse or data lake without prior transformation.
- Transform data on demand within the cloud data warehouse using its built-in processing power.
This ELT workflow is faster for moving large amounts of raw data and supports real-time access for data analytics.
ELT leverages the capabilities of cloud-based data warehouses like Snowflake, which can process massive datasets quickly and flexibly.
What Are the Key Differences Between ETL and ELT?
Let’s look at key differences and advantages of each approach:
Data Volume.
- ETL is best for smaller, structured datasets where data quality matters.
- ELT handles big data and unstructured data, loading raw data for flexible, on-demand transformation.
Data Quality.
- ETL delivers clean, validated, standardised data before loading. Essential for integrating sensitive data like customer records or payments.
- ELT keeps raw data untouched initially, making data management complex but providing transparency for audits.
Performance.
- ELT shines with modern data workloads, using the processing capabilities of cloud data warehouses.
- ETL can be slower due to transformation before loading and reliance on etl servers
Scalability & Flexibility.
- ETL is rigid: predefined transformations, limited adaptability.
- ELT is flexible: transform only the data you need, when you need it, supporting real-time data and ad hoc analysis.
ETL vs ELT Comparison – Which Should You Use?
ETL and ELT are both proven data integration methods. Deciding between ETL vs ELT depends on your company’s specific needs, the nature of your data sources and data sinks, and your existing IT infrastructure.
ETL: Ideal for Structured Data and High Security Requirements
ETL is best suited for scenarios involving structured data where data quality and security are essential. Organisations with strict compliance demands often prefer ETL because it offers better control over the data transformation process and ensures sensitive data is clean and standardised before loading into the data warehouse. It’s also ideal when you need to improve or enforce consistent data standards before storage.
ELT: Flexibility and Speed with Large Amounts of Data
ELT excels when working with large volumes of raw data and modern cloud-based data warehouses. It’s designed for speed and scalability, enabling quick loading and flexible real-time analytics. ELT workflows take advantage of the processing power of the cloud to transform data as needed, making it perfect for big data platforms and companies needing rapid updates or ad hoc analysis.
ELT and ETL: Budget and Resources
Cost considerations are also important when choosing ETL vs ELT. ETL may require more server resources and computing power for the transformation process, but storage costs are lower because raw data isn’t retained. ELTleverages cloud scalability and reduces on-premises hardware needs, but cloud storage costs can rise with large amounts of data.
Whichever approach you choose, both ETL and ELT play vital roles in a robust data integration strategy, ensuring you can move, process, and manage your data effectively for better business decisions.
Lobster’s Data Platform: Your All-in-One ETL/ELT Tool for Data Integration.
Wondering how to implement the right ETL and ELT strategy?
Lobster’s Data Platform is your central solution for modern data integration.
It supports both ETL and ELT workflows, plus EDI and EAI, to cover all your data integration process needs.
Features include:
- Processing structured and unstructured data
- Support for all standard data formats and protocols
- Integration with a wide variety of data sources
- Automation of business processes and recurring tasks in your etl process and elt process
- Seamless integration with your IT infrastructure using over 90 pre-built connectors—no programming required
Ready to streamline your data management and automate your data pipelines?
