Difference between ETL and ELT
ETL is the most common method which is used when we transfer data from a source system to data warehouse. Extract, Transform, and load is a process which involves extracting data from outside sources and transforming it to fit operational needs, then loading it into the target database or data warehouse. To use this approach is reasonable when we are using different database for our data warehouse.
In this scenario, we have to transform data from one place to another, so it's an appropriate way to do the transformation work in a specialized engine.
Extract, load, and transform is a process where data is extracted and loaded it into a staging table in the database. After loading it into the staging table transforms it where it sits in the database and later loading it into the target database or data warehouse.
ETL requires management of the raw data, which includes extraction of the required information and running the transformation to serve the business needs. Each stage such as, extraction, transformation, and loading requires the interaction of data engineers and developers, and dealing with the capacity limitations of the traditional data warehouse. Using ETL, BI users and analyst become habitual to waiting, simple access to information is not available until the whole ETL process has been completed.
In the ELT approach, after extracting the data, we immediately start loading phase, moving all the data sources into a single, centralized data repository. With today's infrastructure technologies are using the cloud, and systems can now support large storage and scalable compute. Therefore, a large expanding data pool and fast processing are virtually endless for maintaining all the extracted raw data.
In this way, the ELT approach provides a modern alternative to ETL, but there are cases when we need to use ELT. ELT should be used in various cases instead of ETL, which are:
Note: When we are using ETL, the transformations are processed by ETL tools, while in the ELT, transformations are processed by the target data source.
Working of ELT
Extract, Load, and Transformation is the process of gathering the information from unlimited sources, loading them into a processing location and transfer them into actionable business intelligence.
Differences between ETL and ELT are:
When ETL is the Right choice?
It depends on the company's existing network architecture, budget, and the degree on which it is already using cloud and big data technologies. But when any or all of the three focus area are critical, then we can think to use the ELT.
When ingestion speed is the priority
When the ingestion speed is the priority, we should have to use ELT. Because ELT doesn't have to wait for the data to be worked-off site and then loaded (here, the loading of data and transformation can happen parallel). Here the ingestion process is faster, and deliver the raw information which is more rapid than ETL.
When more intel is better intel
The advantage of turning data into business intelligence is in the ability to surface hidden pattern into actionable information. By keeping all historical data on hand, an organization can mine along with timelines, sales patterns, seasonal trends, or any emerging metrics, which becomes essential for the organization. In this case, we have the access to the raw data, because data was not transformed before loading. Mostly in the lake of cloud data, raw data is stored and then refined, or processed information is stored. For example, data scientist prefers to use the access of raw data, whereas business user prefers to use the normalized data for business intelligence.
When we will know we need to scale
When we are using high-end data processing engine like a cloud data warehouse or Hadoop, ELT can take the advantage of native processing power for higher scalability. Both ETL and ELT are the time-saving methodology for producing business intelligence from raw data. But the cloud is changing how businesses tackle ELT challenge with all technology.
ETL stands for Extract Transform and Load while ELT stands for Extract Load and Transform. In ETL data flows from the source to the staging and then to the target. In ELT target system do the transformation. The staging system is not involved in ELT. In ELT, we face many challenges, but it is expensive and requires excellent skills to implement and maintain.