What makes ETL Technology Different From ELT?

Technologies such as ELT and ETL are widely implemented in these industries for handling a vast amount of data and converting the data into a usable format for the purpose of data analysis and processing. The types of data are also increasing with each passing day, that includes big data, sensor data, behavioural data, etc. These industries need to use highly advanced technologies to analyze the various types of data accurately.

These two methodologies help industries to handle the data but perform their operations in different ways.

Extract, Transform, Load (ETL) refers to a process of extracting data from various sources and transforming it in such a way that it fits the needs of the business. This method is used for transforming and loading the data to the end databases or a data warehouse. This way, the transportation work of carrying the data from one system of another is optimized, using a specialized engine.

Extract, Load, Transform (ELT) refers to a process of extracting the data and loading it into a database, then transforming it in the staging table of the database and loading it in the target database or a data warehouse.

Understanding ETL and ELT: 

ETL generally includes the management of raw data and extraction of the information that is required by the industries to carry out various operations. This process includes a team of testers, developers, engineers, etc. to carry out data analyses process efficiently.

ELT includes the transportation of raw copy of data from the staging tables to end database in various columns and data fields.

ETL software is used by the industries for computing a small amount of data and intensive transformations of data into a meaningful format.

On the other hand, ELT is used for computing a huge amount of data in the industries.

The transformation time also acts as one of the major differentiators between ETL and ELT.

The transformation time in ETL process is highly dependent on the size of the data. The industries need to wait for the transformation process to be completed. The time increases with the size of the data.

However, the transformation time in ELT is not at all dependent on the size of the data. That means the size of the data does not impact the transformation process of the data.

The amount of flexibility that these two processes possess is also different.

ETL tools are ideal for relational database systems and are not usually used for unstructured data. The industries need to carry out the mapping process for the data that is to be moved to the target database. If the industries need to change the data, then the ETL tools need to restructure the mapping of data and load the data all over again.

ELT tools have the ability to handle structured as well as unstructured data efficiently. The use of this technology ensures the industries to move all the data in the target database without the need for any mapping or restructuring of data. This process makes the data sets much more flexible.

The staging areas also differ in ETL and ELT processes. The staging areas in ETL are included within the ETL tools, between the source and the target system. The staging areas in ELT are included within the data warehouse. Here, the database engines are responsible for performing the transportation process of the data.

ETL software works in a well-defined workflow and extracts the data from homogeneous and heterogeneous sources of information. Then, the data is enriched and stored in a data warehouse or any other storage unit.

ELT works with powerful target systems to handle transformations. ELT works in pipelines and transforms the data in the staging area of the database before loading it again in the target system.

ETL vs. ELT in industry processes: 

ETL process is mainly used by the industries when the data present with them is in a structured format and is low or moderate in volume. Here, the source from where the data is gathered and the target database, where the data is stored is different. The source as well as the target database use different data sets for its operations.

ELT process can also be used on unstructured data. The industries use ELT process where there is a need to analyze and transform large volumes of data. Here, the target database engines are designed to handle a huge amount of data for analysis. In this process, the target as well as the source database are of the same type.  

Both these approaches are revolutionizing the process of data analysis and are overcoming the limitations of traditional approaches. The highly innovative methodologies are helping the industries to carry out the process of data analysis and transformation in an efficient manner.

Rather than using either of the one approaches, the industries are shifting towards combining both these and using them simultaneously, commonly known as ETLT approach, as and when the need arises.

What is ETLT: 

The modern technologies are flexible enough to include ELT as well as ETL methodologies in their operations. The use of the Extract Transform Load Transform (ETLT) approach has enabled the industries to directly join the data from the existing target database to the data that they need to load. There are numerous tools present to carry out this process in an efficient manner by combining the strengths of both approaches in a single unit.

IT applications have been completely transformed with the use of these approaches and have enabled the industries to perform a highly efficient transformation process. The use of highly advanced technologies to integrate the data from different sources and storing the data in a secure unit has become a lot easier than it was before. Now, new data types can easily be managed and transformed with evolving technologies. These approaches have paved a way for the IT industry to maximize data storage, along with simpler transformations of the data.