ETL vs ELT

What is ETL?

Extract — Transform — Load

ETL involves extracting data from source systems, transforming it into the required format in a staging area, and loading it into a target system, typically a Data Warehouse (DWH). Historically, ETL has been the predominant method, especially when the DWH lacked the processing power to handle complex transformations.

What is ELT?

Extract — Load — Transform

With ELT, data is extracted and loaded directly into the target system (usually a DWH). Transformations are then performed within the target system itself. This method leverages the powerful computing capabilities of modern data platforms (e.g., cloud-based solutions) to handle extensive transformations.

ETL Process

Extraction

Raw data is collected from diverse source systems, which may include structured, semi-structured, or unstructured data such as databases, files, SaaS applications, IoT sensors, or application events. At this stage, ETL and ELT methods do not yet differ.

Loading

This step marks the primary distinction between ETL and ELT:

• In ETL, data is first sent to an intermediate server (e.g., data warehouse) for processing.
• In ELT, data is directly transferred to the target system (e.g., data lake), reducing latency between extraction and availability but increasing the load on the target system.

Transformation

Transformation involves structuring and standardizing raw data within a database or data warehouse. Although storage costs may increase, this step enhances capabilities for subsequent BI, data analytics, and reporting processes.
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Transition from ETL to ELT

ETL has long been the standard for data integration, primarily targeting traditional data warehouses. However, rising hardware costs, growing IT demands, and delays in ad-hoc analysis have driven many organizations toward ELT.Shifting from ETL to ELT has become increasingly popular due to improved business flexibility, better scalability, and compatibility with cloud infrastructure.

ETL or ELT: Which Is Better?

Both ETL and ELT effectively consolidate data to derive actionable insights. Choosing between them depends on your organization's specific requirements, such as existing network architecture, use of cloud technology, data volume, source system complexity, and budget constraints.

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