Transform First, or Load First? Comparing ETL and ELT Approaches

[Anand_Rao]

Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) are two common approaches for moving data from source systems into a data warehouse or data lake. Both serve the same general purpose, but work in slightly different ways. In this post, we'll review the key differences between ETL and ELT and when you may want to choose one approach or the other.

[Anand_Rao]

First, let's define the acronyms. ETL stands for Extract, Transform, Load. The extract step collects data from various sources. Next, the transform step cleans, filters, aggregates, and prepares the data for analysis. Finally, the load step inserts the transformed data into the target destination

Conversely, ELT is Extract, Load, Transform. As the name suggests, the order of operations is slightly different. With ELT, data gets extracted from sources and then loaded directly into the target system. Transformations occur later and often within the database itself.

A core difference is where data transformation logic resides. With ETL, transformations typically happen outside the target database in a specialized engine or external programmed logic.

ETL is more flexible and can handle semi-structured and unstructured data like JSON, different text formats, files, images, and video. ELT is better suited for structured data like tables and CSV files.

By cleansing and anonymizing data prior to loading, ETL minimizes the risk of regulated data entering the target system. When there are simpler compliance requirements in unregulated industries that the cloud platform can handle, ELT can be used.

ELT is desired when simplicity in management is prioritized. However, if cloud and data warehouse design and operations management skills are in-house, ETL might come out ahead. 

ETL is a mature technology with wide adoption. ELT is newer but gaining popularity as cloud data warehouses emphasize scalability and flexibility.

For small or mid-sized datasets, especially relational sources, ETL remains a sound choice. But as data volumes grow, ELT becomes more compelling with its faster loading and transformation times.

To summarize some key considerations:

- For data warehousing use cases, ELT is typically better suited, with SQL handling transformations

- In data lake environments, ETL remains preferable in most situations. ETL shines when data movement involves files and transformation is done with Spark processing.

- However, for data lake use cases demanding low latency, by offering the flexibility to consume raw data without transformation.

- When compliance with data privacy regulations is a top priority, ETL's data cleansing protections are appealing.

- ETL requires more upfront investment into integration tools and expertise, while ELT leverages existing infrastructure.

- ETL remains the prudent choice for small, relational data sources.

- As data volume, variety, and velocity increase, ELT becomes more attractive.

The optimal approach depends on your specific data environment, use case requirements, and team skills. ETL and ELT both have pros and cons, and often they can complement each other rather than act as mutually exclusive options. By understanding the key differences outlined here, you'll be better positioned to choose the right strategy or combination of both for your organization's data integration needs.

Whether you choose ETL or ELT, we have an offering for you. Take a guided tour or trial here.