ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) are data integration methodologies that describe the three stages of moving and processing data from source systems to target storage destinations. ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) are acronyms that describe the three stages of a data pipeline. Both the ETL and ELT process involve cleaning, enriching, and transforming data as part of a data pipeline, with the difference being when you transform the data.
Extract
In the extract step, you are pulling data from a source. The data can be structured or unstructured.
Transform
The transform step cleans the data and formats it into a structure that matches the format of the target data storage system.
Load
This is the process of moving the data into the storage destination and analyzing the information with business intelligence tools.
ETL is the most commonly used approach to gather and reformat data. With ETL, data is taken from a variety of sources, and transformed before it’s loaded into a system of defined formats and styles. Afterwards, the transformed data is loaded into a data warehouse. This process is best for small data sets that require complex transformations. Analysis can happen immediately once the process is complete, since the data is transformed before being loaded into the data warehouse.
The ELT Process
In the ELT process, data is extracted from one or more sources and is loaded into the data warehouse without any formatting. It’s a lower-cost process that’s suitable for structured and unstructured data. The ELT process has lower maintenance overhead since data is loaded without being transformed.
Since all the data is loaded into the target database, transformation can happen at any time on an as-needed basis. While there is lower maintenance in loading data using ELT, the costs for storing this non-transformed data can be higher, since the non-transformed data consumes more space. The benefits, however, is that you can conveniently mine this data for relevant business intelligence. This process of mining for business intelligence can be slow if there isn’t sufficient processing power.
The difference between ETL and ELT is when the data is transformed and how much data is retained. There are challenges inherent in both processes. While ETL has been the standard process for data integration, it requires more hardware and IT support and takes longer to complete.
If your priority is speed, ELT is going to be the best choice since the process to deliver raw data is faster. More businesses are shifting to an ELT process thanks to the availability of cloud-based services. However, with ELT you may face challenges with compliance of privacy regulations, such as GDPR and CCPA since you are loading data all at once. The benefit of ETL is that you can eliminate certain data before it’s loaded to avoid privacy violations.
Whether you choose ETL or ELT, it’s essential to use data tools, especially automation, when managing your data pipeline. Strong data governance practices ensure data quality and compliance throughout the process. Your solution will depend on a variety of factors, such as the long-term needs of your business, the type of data you have, and your data storage. Many organizations also use reverse ETL to push transformed data back from the warehouse into operational tools.
FAQ
When should I use ETL vs ELT?
Use ETL when you need to transform and cleanse data before it enters your data warehouse, particularly when dealing with sensitive data that requires filtering for privacy compliance like GDPR or CCPA. Choose ELT when speed of ingestion is the priority and you have a cloud-based data warehouse with sufficient processing power to handle transformations on demand. ELT is increasingly popular because modern cloud warehouses can process large-scale transformations efficiently.
What does ETL have to do with a customer data platform?
A customer data platform (CDP) relies on ETL or ELT processes to ingest customer data from multiple sources — CRM systems, websites, mobile apps, point-of-sale systems, and more — and unify it into a single customer profile. CDPs typically automate much of the extract, transform, and load pipeline, reducing the engineering burden of data ingestion and ensuring that customer data is clean, unified, and ready for activation in real time.
What are the main challenges of ETL and ELT pipelines?
Common challenges include data quality issues from inconsistent source formats, pipeline failures due to schema changes in upstream systems, and scalability bottlenecks as data volume grows. ETL pipelines can become slow and expensive because transformations must complete before data is available for analysis. ELT pipelines may accumulate storage costs from raw, untransformed data and can introduce compliance risks if sensitive data is loaded before proper masking or filtering is applied.
Related Terms
- Data Lakehouse — Modern storage architecture where ETL/ELT pipelines deliver data
- Data Orchestration — Manages and schedules ETL/ELT pipeline execution across systems
- Data Lineage — Tracks data origin and transformations through ETL/ELT stages
- Data Validation — Quality checks applied during the transform step of ETL/ELT