ELT versus ETL: What’s the difference?

ELT (extract, load, transform) and ETL (extract, transform, load) are both data integration processes that move data from a source system to a target system. The key difference between the two processes lies in the order of data integration steps in a data pipeline.

• In ELT, data is loaded directly into a target system and then transformed for its intended purpose.
• In ETL, data is transformed in a staging area before being loaded into a target system.

This key distinction causes other differentiators between the two processes, from infrastructure needs to security considerations. As a result, they serve different data processing use cases and business needs: ELT is often used when data availability is a priority and organizations need to move large volumes of data quickly into cloud-based repositories.

ETL can be preferable when data must be standardized or protected before reaching its target system—for example, in case personally identifiable information (PII) is being transferred and data privacy is paramount. It is also useful when data integration involves legacy systems with limited transformation capabilities.

To make an informed comparison between ELT and ETL, it helps to establish their definitions. 

ELT: In ELT, raw data is extracted from a source system and loaded into a target system. It then undergoes transformation within that target system. Target systems might include cloud data warehouses, data lakes or data lakehouses. 

ETL: In ETL, raw data undergoes transformation outside the final target system in a staging area or other external location. Then, the transformed data is loaded into a target system, such as a target database or a data warehouse. 

The key difference between these approaches to data integration is the order of steps. In ELT, transformation takes place at the very end of data integration after raw data is loaded into a target system. In ETL, data transformation takes place in the middle of the process: after the extraction from sources but before data is loaded to the target system. 

Both ELT and ETL consist of the same three steps—extract, transform and load—but both the order and the nature of each step varies depending on the process.

In both ELT and ETL, the extract step takes place at the very beginning. Raw data is copied or exported from sources that include but are not limited to:

• Relational or NoSQL databases
• Customer relationship management (CRM) systems
• Enterprise resource planning (ERP) systems
• Internet of Things (IoT) devices and sensor data
• Microservices and APIs
• JSON and XML files
• Application logs and event streams
• Flat-file databases
• Email, text and document files
• Web pages

While both processes can extract data from various sources, ETL is traditionally known for its handling of structured data while ELT is often preferred for unstructured and semi-structured data.  

In ELT, loading data to its target destination is the second step. In ETL, it’s the last.  

The nature of the target destinations can also differ. Target destinations for ELT might include cloud data warehouses, data lakes and data lakehouses that provide scalable storage for large volumes of structured and unstructured data.  

Target destinations for ETL traditionally have included on-premises data warehouses and relational databases (especially in legacy systems). Modern ETL is also widely used with cloud-based repositories.  

In the transformation step, raw data is prepared for its intended use, such as data analytics or AI workloads. Transformation can entail: 

• Cleansing data, filtering, aggregating and deduplicating
• Validating data and authenticating 
• Performing calculations, translations or summarizations based on the raw data. This process might include changing row and column headers for consistency, converting currencies or other units of measurement, editing text strings and more. 
• Conducting audits to ensure data quality and compliance and computing metrics 
• Removing, encrypting or protecting sensitive data according to security or governance policies 

In ETL, data transformation typically takes place in a staging area before being loaded into a target system. The transformation process often converts data into a schema compatible with the target system so that the data is ready for use when it is loaded.

In ELT, data transformation takes place within the target system by using SQL-based tools such as data build tool (dbt). Because data is in its raw, original form when initially loaded into the target system, data scientists and other stakeholders can apply their own transformation logic to it to meet specific needs.

Enterprises often use dbt to transform data in data warehouses such as Amazon Redshift and Google BigQuery, data management platforms such as Snowflake and data lakehouses such as watsonx.data® data store. 

Order of operations is the primary difference between ELT and ETL, but it is not the only one. Here is a breakdown of what distinguishes the two processes.

Traditionally, data integration has been a batch-oriented process. In batch processing, organizations aggregate and analyze datasets in batches at fixed intervals. Enterprises can optimize resource use and reduce manual labor by using batch processing to automate repeating workloads, such as the generation of routine reports. 

But as enterprises seek to derive immediate insights from streaming data, batch processing can fall short: Real-time analytics can’t wait for the next scheduled run.  

In these cases, distributed event streaming platforms (such as Apache Kafka) can support continuously running data pipelines and event streams that enable real-time data processing. These architectures can apply ETL or ELT data integration to the continuously arriving data.

While Kafka is an open source solution, enterprise-grade solutions like Confluent, an IBM company, can enhance Kafka implementations with greater data management capabilities, secure governance, pre-built connectors and customer support.

As distinct data integration processes, ELT and ETL bear their own specific advantages and disadvantages.

• Faster data movement: Because ELT does not transform data in transit, data arrives faster for time-sensitive use cases. 
• Higher pipeline resilience: Because loading occurs before transformation, a transformation error is less likely to disrupt the pipeline.
• Data reusability: Different stakeholders can access the original, raw data from the target system and apply their own transformation logic to it.
• Greater scalability: ELT leverages the power and size of cloud-based repositories to enable transformation at scale. 
• Cost efficiency: Cost efficiency can be achieved through lower upfront costs and by using flexible cloud compute and storage resources on an as-needed basis.  

• Data quality, compliance and security: Without in-transit transformations to clean and organize data, enterprises might encounter data quality, compliance and security concerns in the raw data delivered to target systems.
• Storage and compute costs: While using elastic cloud resources can be cost-efficient, costs can still increase over time as data volumes grow.
• Legacy infrastructure: ELT works best with modern cloud infrastructure, rather than legacy systems with more limited storage and capabilities.

• Data quality improvement: ETL can improve data quality during the transformation phase through methods such as data cleansing and deduplication.
• Compliance support: ETL is often useful in regulated industries because compliance and security measures, such as encryption, can take place before data is delivered to its destination.
• Legacy system compatibility: Organizations that use legacy systems and on-premises servers can deploy ETL to ensure data is transformed as necessary to match target system requirements.
• Reducing cloud storage needs: Data transformation techniques such as filtering can decrease the volume of data loaded into cloud platforms, helping lower storage requirements and costs.
• Support for AI workflows: Streaming ETL enables continuous delivery of high-quality data to AI and machine learning workflows, which can be used to monitor drift and improve prediction accuracy.

• Upfront preparation: Traditional ETL required provisioning secondary processing servers for staging and transformation, although modern services might sometimes reduce infrastructure issues. ETL also generally requires the establishment of business rules and transformation logic before integration begins.
• Infrastructure and labor costs: While ETL can help enterprises reduce the costs of cloud compute, it might require greater upfront investment on infrastructure and pipeline design by data engineers in comparison to ELT.
• Schema management: Unexpected changes in the structure of source data—such as the renaming of columns or alterations to data types—can cause pipeline failures.

Leading data integration solutions often offer the flexibility to support both ELT and traditional ETL pipelines. Other key features might include:

• A broad set of connectors to ingest data from disparate data sources
• Integration with transformation tools, data warehouses and data streaming solutions
• No-code and low-code options and AI-assisted pipeline design, enabling more users to build pipelines faster
• Comprehensive automation that can automate the entire data flow
• Integrated observability, quality, lineage and governance for more reliable pipelines
• Security features that encrypt sensitive data—both in motion and at rest—and help organizations maintain compliance with industry or government regulations such as HIPAA and GDPR.

An ELT process is well-suited to high-volume datasets or real-time data environments.

Integration of large volumes of data: Meteorological systems such as weather services regularly collect, collate and use large amounts of data. Businesses with large transaction volumes also fall into this category. The extract, load and transform process allows for quicker transfers of source data.

Fast access to data: Stock exchanges generate and use large amounts of data in real time, where delays can make a significant difference. Also, large-scale distributors of materials and components need real-time access to current data for business intelligence.

ETL is useful for synchronizing multiple data environments, migrating data from legacy systems and securing data in motion.

Syncing data from several sources: Companies that are merging their ventures might have multiple customers, suppliers and partners in common. This data can be stored in separate data repositories and formatted differently. ETL works to transform the data into a unified format before loading it into the target data location.

Data migration from legacy systems: ETL can be useful when legacy system data needs to be cleaned or reformatted into a compatible format before loading into the target system.

Transfer of sensitive data: Highly regulated industries such as healthcare and financial services can ensure that sensitive data is encrypted or masked in transit, during the transformation phase of ETL.

ELT is often appealing when business needs include managing large volumes of data, moving data quickly and retaining data in its raw form (for later reusability) within the target system. ETL might be preferred when moving data from or between legacy systems, ensuring sensitive data is secured in transit and unifying disparate data before it arrives at its target destination. Other considerations include issues such as latency, cost, governance and existing infrastructure.

Yes, ETL can be used to move data to cloud environments. While ETL predates the widespread adoption of cloud computing, modern ETL software and tools enable data migration and integration into cloud data platforms and warehouses. However, enterprises seeking to defer data transformation and quickly transfer large volumes of data into cloud-based repositories often choose ELT instead for the sake of speeding data movement.

ETL is often associated with transferring data from legacy systems. If businesses eventually discard those systems, some might wonder: What becomes of ETL? However, the process’ security benefits and its ability to land clean, usable data in target destinations suggest that it will remain useful. Organizations might use both ETL and ELT depending on workload requirements, governance or target-system capabilities.