IS ELT BETTER THAN ETL?
is ELT is know as Extract, Transform, Load while ELT stands for Extract, Load, Transform. Whats the difference between the them? Now these both techniques are data Integration Techniques. Now What is data Integration? The concept of data should move freely and should be more available comes from data integration. Data Integration also refers process of bringing data through multiple sources all together and providing the user with a unified view ie. A single view. Now lets jump back to ETL.
E — Extract refers to the process of pulling the data from a source for example as an SQL database, XML file, Cloud Platform.
T — Transform refers to the process of converting the format or structure of the dataset to match that of a target system.
L — Load refers to the process of placing a dataset into target system.
Now lets dive deeper into the ETL process, after the first process that’s the Extraction process, The data has to be taken to the Transform according to the needs of the Target system. This transformation takes place into a stagging area outside the warehouse and it follows a very simple rule all the data should be transformed before loading. Now, just think if you have a larger dataset how long it will take the data to transform? Obviously more also more computational power will be needed by the system. So it can be said that ETL process takes longer time and more power when working with bigger dataset. Small datasets that require complex transformation ETL is an appropriate process. ETL process moves data from its original source to the data warehouse. Consider an example of ETL in action. Online Analytical Processing (OLAP) data warehouses only accept relational SQL-based data structures. With this kind of data warehouse, a protocol such as ETL ensures compliance by routing the extracted data to a processing server, and then transforming the non-conforming data into SQL-based data. The extracted data only moves from the processing server to the data warehouse once it has been successfully transformed.
ELT stands for Extract>Load>Transform. The words mean the same as above but the process is quite different. ELT uses the target system itself for the transformation, and does not require stagging like ETL does. All of the data cleansing, data enrichment, and data transformation are done within the data warehouse itself with ELT data pipeline. As raw data is stored indefinitely in the data warehouse, multiple transformations can be performed on it.
Scalable cloud-based data warehouses were new development which were made possible by the ELT. In terms of storage and processing power, cloud data warehouses like Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure provide the digital infrastructure necessary for raw data repositories and in-app transformations. As companies adopt cloud infrastructure, ELT data pipelines are becoming more popular, even though they aren’t used universally.
Why is the market shifting from ETL to ELT?
The answer is very simple companies have a large dataset to be transformed what happens in ETL is all the data is first transformed into the consumers expectations and then loaded into the data warehouse while in ELT The data is directly loaded into the data warehouse and all the transformation takes place in the data warehouse and the best thing is the consumer can see the data and understand what data needs to be transformed simultaneously to get that questions answered. ELT is quicker and transparent as well.
In summary, ETL stands for “Extract, Transform, Load.” It is a process in data warehousing where data is extracted from various sources, transformed into a common format, and then loaded into a data warehouse. The data is transformed to meet the requirements of the target system before it is loaded into the data warehouse.
ELT stands for “Extract, Load, Transform.” Unlike ETL, ELT involves loading the raw data into the data warehouse and then transforming it into the desired format using the processing power of the data warehouse. This approach is gaining popularity due to the increasing processing power of modern data warehouses and the need for real-time insights.
In summary, the difference between ETL and ELT lies in the order of transforming and loading data into the data warehouse
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