Difference between ETL vs ELT
In this Topic, we are going to learn about ETL vs ELT but lets first discuss what process of E, T, L stands for,
- Extraction: The source data is pulled from the data pool in the extraction stage, the pool may be unstructured. next is the process of pushing the data into a staging data repository.
- Transformation: This is the procedure of making over or elevating the data so that it obtains to be suitable for the target source.
- Loading: It is the route of poignant data into a data warehouse so that necessary business intelligence tools can be applied on top of this.
ETL: The ETL process involves extracting the data from classified data sources and then to transform and tether the data in a suitable manner, lastly the data is been loaded into data warehouse systems. This Technique is sensible until many dissimilar databases are implicated in the data warehouse landscape. here moving data from one place to another has to happen at anyway so ETL acts as the best practice at these situations to do transformations since the transfer of data is anyhow happening instance here
ELT: It is a slightly different process, The same technique of extract is used here, next the data is loaded into the target systems directly. At the preceding end, the objective systems are accountable for applying the transformations at the loaded data. The major disadvantage here is it usually takes larger time to get the data at the data warehouse and hence with the staging tables an extra step is added in the process, which makes in need for more disk space be available.
ELT plays its role in the following instances,
- When the major priority is the ingestion speed. Since off-site loading is not happening here this is considered a very fast process, hence necessary information is passed on very faster here than ETL. ELT also has the advantage of decreasing the dispensation happening at the source in view of the fact that no transforming is performed
- The benefit of turn-off data keen on business intelligence lay in the capacity to face unseen patterns into actionable information. By observance, every bit of historical data on tender, organizations can dig on timelines, seasonal trends, sales patterns or any promising metric that turn out to be important to the organization. Since no transformation on the data before getting it loaded, there exists access to all raw data available.
- When there is a need for scalability. When top-end data processing engines come into play then ELT is the better option to go with, ELT is able to obtain an improvement of the inhabitant dispensation power for higher scalability.
ELT has the advantage of decreasing the dispensation happening at the source in view of the fact that no transforming is performed, this is very important to be considered if the source is a PROD system. The major disadvantage here is it usually takes larger time to get the data at the data warehouse and hence with the staging tables an extra step is added in the process, which makes in need for more disk space be available.
Head to Head Comparison Between ETL vs ELT (Infographics)
Below are the top 7 differences between ETL vs ELT
Key Differences Between ETL vs ELT
There are major key differences between ETL vs ELT are given below:
- ETL is an older concept and been there in the market for more than two decades, ELT relatively new concept and comparatively complex to get implemented.
- In an ETL case, a large number of tools have only one of its kind hardware requirements that are posh. In the case of an ELT Since this falls under Saas hardware cost is not a concern.
- To carry out a lookup, ETL operates row by row pattern to map a fact-value with its dimension key element from a different table. In ELT we can directly map fact-value with dimension key elements.
- In ETL Relational data is prioritized here, whereas ELT Readily supports unstructured data.
Comparison table between ETL vs ELT
Let’s discuss the top 7 difference between ETL vs ELT
| Basis of comparison between ETL vs ELT | ETL | ELT |
| Usage | Implying complex transformations involves ETL | ELT comes into play when huge volumes of data are involved |
| Transformation | Transformations are performed in the staging area | All transformations in target systems |
| Time | Since this process involves loading the data into ETL systems first and then into the respective target system this pulls in a comparatively larger time. | Here since data is directly loaded into the target systems initially and all transformations are carried out at the objective systems. |
| Datalake involvement | No data lake support | Unstructured data can be processed with data lakes here. |
| Maintenance | Maintenance is high here since this process involves two different steps | Maintenance is comparatively low |
| Cost | Higher in the cost factor | Comparatively lower in cost |
| Calculations | Either we need to override an existing column or there is a need to push data at the targeted platform | The calculated column can be easily added |
Conclusion
Every company complied with data warehouse will be using ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) to push data into the data warehouse which is emerging from different sources. Based on the industry and technical wants, one among the above procedures is widely deployed.
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