The ETL and ELT are databases used to extract, transform and load data into warehouses. Changing one of these processes can completely change the final product.
Modern analytics processes large volumes and different types of data, which is slower with ETL than with ELT.
The ETL process is therefore a novelty in the data world. It is a scalable, modern and flexible approach that allows today’s businesses to compete in the market.
Find out what really changes in data management by moving redundancy from ETL and ELT.
Extract, Transform, Load: What Is ETL?
ETL is a data pipeline that combines data conversion processes in three separate steps:
- Extracting data from different and diverse sources
- Transforming data for use
- Transfer to a data warehouse in the cloud or on-premises.
The ETL process is traditional and familiar to those in the fiend.
In the extraction stage, data is collected from various sources such as spreadsheets and CRMs. After extraction, the data is converted into a format that is available for analysis. Finally, it is transferred to a data warehouse where it is stored and made available for quick use.
The main purpose of ETL is to collect relevant data, prepare it for use in reports and archive it for easy access and further analysis. This process allows experts and developers to focus on other tasks.
Extract, Load and Transform: What Is ELT?
Unlike the more traditional and popular ETL, ELT is an extension of this process, making it more flexible by changing the data conversion steps.
In this data management model, the steps are arranged in the following order:
- Extracting raw data from different and diverse sources
- Loading the extracted data into data warehouse
- Transforming the raw data into data to be modeled in data warehouse
This simple inversion, for example, reduces the data loading time and allows company experts to work with the information directly in the data warehouse without the need to employ high-tech experts such as programmers and data engineers.
This allows a better division of labor: data engineers deal with the extraction and loading phases, while experts more familiar with business rules, such data scientists and analysts, deal with the other phases.
Apart from the division of labor, the reverse process from ETL to ELT has other consequences for the final product.
7 Difference Between ETL and ELT Data Management Conversion Process
ETL and ELT are data lines used to export, transform and load data into a file. Changing just one of these processes can completely change the final product.
1. Conversion Time
In ETL, the conversion time increases significantly as the amount of data increases.
In contrast, in ELT, the transformation phase is faster because the cloud infrastructure technology is used. In this case, the speed does not depend on the size or complexity of the data.
2. Service Time
The maintenance time of ETL is high because it requires the regular work of expensive and rarely used specialists such as IT specialists or programmers to update the data set.
With ELT, the scenario changes as the data is always ready and available for use in the data warehouse.
3. Loading Time
With ETL, data is loaded only after the conversion, so each step requires a different device, and the execution takes longer because the load process must be repeated for each data conversion.
In ELT, data is loaded once into a data warehouse, where it is converted for use.
4. Easy Of Use
ETL is primarily used by IT professionals, programmers, engineers and computer scientists using spreadsheets and fixed
On the other hand, ELT is scalable, flexible and interoperable and can be used by both technicians and end users.
5. Complexity Of The Application
ETL initially requires less storage space.
ELT requires a thorough knowledge of modern tools for advanced analytics and a well-structured data warehouse architecture.
6. Maintenance Of The Data Warehouse
ETL is designed to support relational databases, local databases and legacy systems.
The ELT, is designed to handle large amounts of data, structured or unstructured, and multiple data sources in a scalable manner across cloud infrastructures.
7. Cost Benefits
For SMEs, ETL is not necessarily a cost-effective approach due to the factors mentioned above, such as high operational costs.
As ELT is scalable, configurable and affordable for businesses of all sizes, it is a much more cost-effective, economical and modern solution.
Does Your Company Need ETL Or ELT?
Regardless of the size of your business or how you create value from your data, a data management system is critical to the success of your business.
Contact Existbi’s team of experts to implement ETL or ELT processes for your business, depending on your needs.
ExistBI is technology- and vendor-neutral, which means that your interests always come first and that you can work with a data warehouse consulting service provider to design and implement the best solution for your specific needs.