What is ELT? Definition, use cases, & benefits

ELT is an alternative to extract, transform, and load (ETL), mainly used with data lake implementations. In ELT, data is stored in its original raw format without being transformed upon entry into the data lake, allowing faster loading times.

The ELT process

Extract

Data is extracted or copied from various source systems like databases, files, APIs, etc. This is the same as the first step in ETL (Extract, Transform, Load).

Load

The extracted raw data is loaded directly into a target data warehouse or data lake without any transformation. This differs from ETL, where the data is first transformed before being loaded into the target system.

Transform

Once the raw data is loaded into the target data warehouse or data lake, it is then transformed or processed using the computing power of the target system. This could involve cleaning, deduplicating, joining data from multiple sources, applying business rules, creating new calculated fields, etc.

ELT benefits

• Faster loading: Since data is loaded in its raw form without any transformation, the loading process is much faster than ETL, where transformation is done before loading.
• Scalability: ELT leverages the powerful computing resources of modern data warehouses and data lakes to perform transformations, making it highly scalable for large data volumes.
• Data flexibility: Having raw data available in the data warehouse/lake allows for more flexible data exploration, transformations, and analysis without being limited by pre-defined ETL processes.
• Cost-effective for big data: ELT is often more cost-effective than ETL for processing large volumes of unstructured or semi-structured big data.

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ELT use cases

Large data volumes

ELT is well-suited for handling massive volumes of structured and unstructured data, such as data generated from sensors, system logs, clickstreams, etc. Loading raw data directly into the data warehouse/lake and leveraging its computing power for transformations makes ELT highly scalable for big data use cases.

Real-time data access

For businesses that require instant access to data for real-time analysis or decision-making, ELT is a better approach than ETL as it avoids delays from pre-loading transformations. The raw data is available immediately in the data warehouse/lake for transformations and querying.

Unstructured data

ELT works well for ingesting and transforming unstructured or semi-structured data formats like XML, JSON, etc. The raw data can be loaded into the data warehouse/lake and then parsed or transformed as needed using its processing capabilities.

Cloud data warehouses

ELT is particularly beneficial when using cloud-based data warehouses or data lakes that offer scalable storage and computing resources to perform transformations efficiently on the loaded raw data.

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