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Difference between ETL and ELT

Harina Rastogi
Jul 19, 2022

“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.”

— Chris Lynch

What is ETL and ELT?

Before talking about ETL or ELT let us learn the three fundamental concepts that are common in both. First one is Extract which is about pulling the relevant data from multiple sources.

The next one is Transform which means cleaning, processing and simplifying the data and formatting it. The last one is Load which is putting all the transformed data into the final destination or setting.

These three are the basic concepts that are the same in both ETL and ETL. Now let us learn about both of them in detail.

First one is ETL which means Extract, Transform and Load. In this ETL which extract the data like images, emails or other unstructured data from various sources and then transform the data.

A simple example is an employee database and sales database. Employee database is updated not very frequently but the sales database has to be updated frequently. The last thing done by ETL tool is putting all the data in the warehouse.

Next one is ELT which means Extract, Load and Transform. You might notice the only change in both is in the sequence of transform and load. There are two main reasons for shifting from ETL to ELT.

More use of Unstructured data

Increase in cloud based systems for storage.

When the data is unstructured and in multiple formats then it is difficult to transform it. In such cases, ELT tools come handy. ELT is much faster and has the potential to transform the unstructured data at a better pace then ETL.

Here the sequence is extracting the data and then loading it in the final server and then transforming it. It saves a lot more time and makes it much faster as compared to ETL.

Also Read | Data Ingestion

Advantages and Disadvantages of ETL and ELT

Advantages of ETL

Data Warehouses

Storage space costs a lot. But with the help of ETL you can lower the costs of storage. ETL tools help to filter the data so that only the required data has to be stored which will cost relatively low as compared to storing a lot of data.

Compliance

Privacy concerns are always there when we talk about data and computers. Data encryption is important to protect sensitive user information. In such cases also, ETL is very capable.

Disadvantages of ETL

Slow

Here loading is done after transformation so the wait is a bit longer, making the whole process slow.

High cost

Since transformations are defined initially, the initial cost involved in ETL is high. It is a big disadvantage.

Flexibility

ETL has less flexibility. Because if data sources are changed then transformations have to be configured accordingly. The system is not flexible enough to configure itself.

Maintenance

Since, ETL tool is not flexible, the maintenance needed is much more and it is one of the biggest drawbacks of using ETL. Based on the source data everything has to be updated and maintained.

Advantages of ELT

Fast

ELT is so much faster than ETL. It helps you in loading the data at a great pace. Fat loading means faster transformation.

Less cost

As compared to ETL, the initial costs involved in ELT are very much lower. Transformations are not defined in ELT which saves cost.

Less maintenance

Here the process of data loading and transformation is much simpler so less maintenance is needed.

Scalable

Here scalability is high. You can increase the data usage and simultaneously the space or storage will also increase.

Flexible

Since transformations are not defined, there is flexibility as to when, where and how.

Disadvantages of ELT

Security concerns

When a huge volume of data is dealt with, security concerns arise. What can be done? To mitigate these risks, external managing applications have to be installed. It is a big disadvantage of ELT.

Compliance issues

As we know that in ELT, data is first loaded and because of this there are certain compliance issues. Many warehouses have in-built compliance protocols but some do not. So, the ones which do not have this feature are not totally safe and extra cost is required for their installation.

Suggested Blog | Data Processing

Comparison between ETL and ELT

Both ETL and ELT are different in the way they are implemented. The first main difference lies in the fact that in ETL, the data is first transformed and then loaded in the final system whereas in ELT, raw data is loaded and then transformed.

The second main difference between both is where the data is transformed. In the first the data is transformed in the separate server but in the latter, the data is transformed in a data warehouse.

Apart from these two main differences there are some small points of comparison between ETL and ELT are given below:

Definition

ETL- Data is extracted through a source then it is transferred to a secondary system and the last stage is to load it into the system.

ELT- Similar to ETL, data is extracted through a source but first it is loaded to the system and then it is transformed in the same system.

Extract

ETL- API connectors are used to extract raw data.

ELT- Here also, API connectors are used to extract the raw data.

Transform

ETL- Processing server is the place where raw data is transformed.

ELT- Here, data is first loaded to the final system and then transformed in the final system itself.

Load

ETL- In case of ETL, processed data is loaded in the final system or the destination system.

ELT- Here raw data is loaded in the system and then transformed.

Speed

ETL- It is a very time intensive technique as the data is first transformed and then loaded in the final system.

ELT- It is faster in comparison to ETL. Reason being the way the data is loaded. Here the first focus is on loading the data into the system and then transforming it. It saves a lot of time.

Maturity

ETL- This process is known for over 20+ years and all the techniques used in it are documented and well known.

ELT- It is a tad newer form of integration as compared to ETL. As it is newer, documentation is also not very much and less experienced people are there.

Privacy

ETL- It helps HIPPA. As data is transformed before loading it reduces the PII.

ELT- Here raw data is directly loaded in the systems therefore more safety and precautions have to be taken to eliminate risks and threats.

Maintenance

ETL- Since a secondary server is present, the cost increases and maintenance cost often creates a burden.

ELT- It has very few systems so maintenance is quite easy and cost effective.

Code based transformation

ETL- Here code based transformations are performed on a different server. So, it is best for computing intense transformations and even the pre-cleansing.

ELT- In this everything is done simultaneously. Loading and transforming is done side by side. It is more speedy and effective in every way.

Cost

ETL- Cost burdens and issues are created because multiple servers are needed and more maintenance is required.

ELT- Cost wise it is preferred as it is cheaper as compared to ETL.

Requeries

ETL- Here raw data can never be requeried as it is loaded after transformation.

ELT- Here raw data can be requeried as it is loaded first. So endless requeries possible in case of ELT.

Data output

ETL- In the case of ETL it is always structured.

ELT- In the case of ELT it can be semi-structured, unstructured or even structured.

Data lake compatibility

ETL- There is no data lake compatibility in case of ETL.

ELT- In the case of ELT, data lake compatibility can be present.

Data Volume

ETL- When the transformations are complicated and the data set is small ETL is the perfect choice.

ELT- Where speed and efficiency is needed and data is in huge volume, ELT is the perfect choice.

Also Read | Ultimate Guide To ETL Tools

Examples of ETL and ELT

“Data really powers everything that we do.”

— Jeff Weiner

Both ETL and ELT can be used in multiple scenarios of day to day. Let us take basic examples of both to get a better understanding.

Example of ETL

In data warehousing ETL is used very commonly. In shopping malls if the manager or any person wants to get historical data related to sales, purchase, customer entries or any related thing then ETL comes very handy. By following the sequence of ETL data can be generated and reports can be prepared from it.

Apart from data warehousing, ETL can be used in Data migrations, third party management and even data migrations during merger and acquisitions. For all these purposes, ETL tools are the best because they transform the data initially and then load it.

Examples of ELT

When we talk about ELT, there are two areas in which these tools are used most commonly. One area is where there is a huge volume of data and the other one is the real-time need of data. Talking about specific organizations then -stock exchanges are the perfect examples.

Apart from them big stock related industries, weather related service industries, astronomology labs etc also use ELT. We can say any industry that needs data on a real time basis can use ELT. Or if heavy data is there and processing via ETL is taking time then ELT is the best substitute for it.

There are many factors on which data integration is dependent. People often confuse themselves while making a choice between ELT and ETL. But they should always look at the cost, performance, need and the security involved in both tools and then make a decision.

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Difference between ETL and ELT

What is ETL and ELT?

Advantages and Disadvantages of ETL and ELT

Advantages of ETL

Data Warehouses

Compliance

Disadvantages of ETL

Slow

High cost

Flexibility

Maintenance

Advantages of ELT

Fast

Less cost

Less maintenance

Scalable

Flexible

Disadvantages of ELT

Security concerns

Compliance issues

Comparison between ETL and ELT

Definition

Extract

Transform

Load

Speed

Maturity

Privacy

Maintenance

Code based transformation

Cost

Requeries

Data output

Data lake compatibility

Data Volume

Examples of ETL and ELT

Example of ETL

Examples of ELT

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