ETL vs ELT vs Reverse ETL vs Zero ETL: Differences and Use Cases

 





Data is the backbone of each and every organization today. And this data, lying in siloes is of no use. Hence, integrating all of this siloed data is extremely important to actually do something out of that data. And to integrate this data, various methodologies have emerged. A few of them are ETL, ELT, Reverse ETL and Zero ETL. These methodologies are like the four options that you used to get in the Multiple Choice Questions (in probably the third grade?).
Before any organization takes a decision of which one they must choose, it is crucial to understand what these methods are, how to use them and how they are different from each other- Difference between ETL and ELT is the most important out of all. They can’t be blindly filling out the circle of the OMR sheet like you used to do (in probably the third grade?).
In this blog, we will explore these data integration techniques, highlighting their unique characteristics and appropriate use cases.

ETL (Extract, Transform, Load)

What is ETL?


ETL stands for Extract, Transform, Load. This traditional method involves extracting data from various sources, massaging and transforming it into a suitable format requited by systems, and loading it into a data warehouse or database.

This is how it works-


Zero ETL is an emerging concept where data integration happens without the traditional ETL processes. Instead, data is directly queried and analyzed from its original source. This approach aims to minimize or eliminate the traditional ETL process by integrating data directly between systems in real-time or near real-time. It typically involves the use of data integration platforms or tools like 200 OK that allow seamless data flow and transformation on-the-fly without the need for intermediate staging or batch processing. Zero ETL focuses on reducing latency and complexity in data integration processes.

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