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Difference between ETL and ELT

ETL

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.

Difference between ETL and ELT

ELT

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:

  • ELT is used when the volume of data is high.
  • When the source database and target database both are same.
  • When the database engine is well adapted for processing, such as PDW, with the help of ELT, it is easy to load a massive amount of data very quickly.
Difference between ETL and ELT

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.

  • Extract: Extract the data from different data source which work similar in both data management approaches.
  • Load: ELT delivers the whole data to the site where it will live. ELT shortens the cycle between the extraction and delivery, but there is a lot of work which should be done before the data becomes useful.
  • Transform: Here, data warehouse and database sorts and normalize the data. The overhead for storing this data is high, but it comes with more opportunities.

Differences between ETL and ELT are:

PARAMETERS ETL ELT
Process Data is transferred in staging server and then moved to Data Warehouse Database. Data remains in the DB of the data warehouse.
Transformation Transformation is done in the ETL server and staging area. Transformation is performed in the staging area.
Code Usage ETL Used For:
  • A small amount of data
  • Compute-intensive transformation.
ELT Used For:
  • The vast amount of data.
Load-Time Firstly the data is loaded in staging and later loaded in the target system. It's a time-consuming process. In ELT, data is loaded in the target system only once. Time taken in this process is less.
Transformation-Time ETL process needs time for transformation completion. As the data size grows, transformation time increases. In ELT process, speed never depends on the size of the data.
Maintenance-Time It needs high maintenance as we select the data to load and transform. ELT needs low maintenance as the data is available all the time.
Implementation Complexity In ELT, it is easier to implement it at an early stage. To implement the ELT process organization should have in-depth knowledge of expert skills and tools.
Data Lake Support ETL does not support Data Lake. ELT allows the use of Data Lakes with unstructured data.
Support For Data warehouse ETL model is used for relational and structured data. ELT used in scalable cloud infrastructure which supports both structured and unstructured data.
Complexity ETL process loads only the essential data, which is identified at design time. ELT involves development only form the output- backward and loading only relevant data.
Cost In the ETL process, the cost is high for small and medium business. ELT include low entry costs using online software as a service platform.
LookUps In the ETL process, dimension and facts need to be available in the staging area. In ELT, all data will be available because extract and load occur in one single action.
Calculations In ETL, the existing column is overwritten or need to append the dataset and push to the target platform. In ELT, it is easy to add the column to the existing table.
Hardware In ETL, the tools have unique hardware requirement, which is expensive. ELT is a new concept, and it is complex to implement.
Supports For Unstructured Data ETL supports relational data ELT helps in unstructured readily available data.

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.

Conclusion

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.


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