When data becomes the key to operating your business, an effective data integration process becomes necessary. A data integration methodology ensures that the data is properly collected, transformed, and securely stored for further use. ETL and ELT are two commonly used data integration approaches adopted by businesses.
There are several differences between ETL and ELT, but the basis of the comparison, ETL vs. ELT, is data transformation. ETL involves data transformation before storing it in a data warehouse. On the other hand, ELT includes data storage in the data lake before it is transformed for any use. Read ahead to learn which process or approach is best for you.
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ETL stands for Extract > Transform > Load, a data integration approach. This approach begins by accessing or collecting data from multiple sources, such as applications, sensors, IT infrastructure, and third-party partners.
The collected data is then processed or transformed to make it compliant or compatible with the data warehouse. Once the data is converted into the required format, it is loaded and stored in the data warehouse. Here is an example to help you understand.
When the unstructured data is collected from various sources, it has to be converted into a specific format (SQL-based data) to be stored on the Online Analytical Processing (OLAP) data warehouse, which only accepts relational SQL-based data. The unstructured data extracted from the sources is fed to the processing server to convert it into SQL form.
ELT is another data integration approach in which data is accessed and stored in the data warehouse or data lake in its native format. The data is not transformed before storage, which is why it is expanded as Extract > Load > Transform.
This is a relatively new method that has emerged with the help of scalable cloud-based data warehouses. ELT allows for data cleansing, enrichment, and transformation all inside the data warehouse, providing the liberty for multiple transformations.
Now that we understand the basics of data integration services and methods, let’s compare them. There are several parameters on which these two can be compared.
The ETL process transforms the data on a secondary processing server and loads it into the data warehouse.
On the other hand, the ELT process takes raw, unstructured data and loads it directly into the data lake. This data can be accessed multiple times and used for different transformations.
The second difference between ETL and ELT is the data compatibility. In ETL, only structured data is stored in the data warehouse. It converts the accessed data into a structured form and stores it for analytics.
In the case of ELT, the data lake can be loaded with unstructured data, such as images or documents, in raw format. Once you have this data in the data lake or warehouse, you can use it to transform it into the format you need. You may need professional data lake consulting services for this.
In the case of speed, ETL is slower than ELT. In the ETL approach, there is an additional step where the data is transformed before loading it into the target location. This process can slow down the data pipeline when the data size increases. It will also be difficult to scale such a system.
In contrast, the ELT approach takes in the data and feeds it directly to the cloud data warehouse. The best thing about the cloud data warehouse is that data loading and processing can happen simultaneously, leading to better speed. The processing power and parallelization of cloud warehouses can also make real-time analysis easier.
Whenever data is involved in any process, the need for robust security features automatically arises. ETL is a Schema-On-Write approach, where developers have to build custom security solutions, such as masking PII, to secure and monitor data.
On the other hand, the ELT data integration process has robust security features, such as
These features can safeguard data while you focus on data analysis and its use.
Here is a tabular representation of the key differences between ETL and ELT processes.
Cloud data lake architecture and data warehouse are crucial in the data pipeline architecture. Therefore, it is necessary to discuss these aspects.
When comparing a data lake vs. data warehouse, data lake is more of a centralized, cloud-based repository that stores vast amounts of raw data in its native form. This data can be used multiple times and for diverse analytical purposes. You can store structured, semi-structured, and unstructured data in a data lake without defining schemas.
A data warehouse storage system stores structured and processed data for specific business intelligence use cases. Unlike data lakes, which are cost-effective, data warehouses can be cost-intensive as they involve specialized storage and processing capabilities.
Ideally, ELT is the common choice made by businesses for modern-day analytics needs. However, there are some use cases where ETL holds preference over ELT.
Here are some use cases why ELT is better than ETL.
Both data integration methodologies have their advantages and disadvantages. Here is a quick overview!
Not that you are clear on the difference between ETL and ELT, it will be easier for you to shortlist the data integration methodology for your business applications. There are several ways to implement these approaches. However, you may have to hire a dedicated team of developers for the same, which can be cost-intensive.
If you want a cost-effective solution with the same level of services, you can opt for data warehousing consulting services from XByte. XByte Analytics is a professional agency that excels at deploying, maintaining, migrating, and updating data warehouses that meet companies’ specific requirements.