Traditionally, companies use ETL pipeline to connect production systems with data warehouses. This however has changed a lot in recent years since businesses require more flexibility and cost saving.
From the technical perspective, there are two main differences between ETL and ELT,
- ETL pipelines transform data on a third-party server, while ELT pipelines transform data within a warehouse.
- ETL pipelines transfer data into the warehouse after transformation, while ELT pipelines transfer raw data into the warehouse.
Benefits of ELT
- No need for a separate storage and computation, the work is done by the computational system itself.
- Data transformation and loading happen in parallel, so less time and resources are spent (as only filtered, clean data is loaded into the target system)
- ELT works with high-end data engines such as Hadoop cluster, cloud or data appliances. This gives is additional performance and security.
- The processing capability of data warehousing infrastructure reduces time that data spends in transit and makes the system more cost effective.
- Low maintenance after the system is set up
Drawbacks of ELT
ELT is not all perfect.
- A lack of comprehensive run-time monitoring statistics and information.
- There’s also a lack of modularity because of set-based design for optimal performance and the lack of functionality and flexibility resulting from it.
- ELT requires higher understanding of the data itself since load and transform happen at once. If performed by an engineer, he/she would need business knowledge about the data.
Comparison between ETL and ELT
What to use for your data projects?
With ETL, analysts need to foresee every step of the data pipeline prior to transformation. Engineers are required to integrate, clean, and prepare data for per an analyst’s request. This of course would require a lot of upfront cost. Moreover, it would cost more efforts and resources and make changes.
In the modern era of data engineering however, data stack has become much more integrated and flexible to business use cases. Thousands of data analysts today are using Acho to warehouse, integrate, transform and analyze their data without having to set an ETL pipeline. All data is hosted and transformed on cloud within one system. To learn more about how Acho does ELT for data-intensive projects, check out https://acho.io/data-engineering