Introduction to ETL

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OLAP or Online Analytical Processing Data Warehouses need to work with Relational SQL-based Data Structures irrespective of whether they’re Onsite or Cloud-based. Hence, any data you load into your OLAP Data Warehouse needs to be converted into a Relational Format before the Data Warehouse can consume it. This is where ETL comes in.

Here are a few advantages of ETL:

  • Data Warehouse Storage Management: If your data storage is a cost-effective system, using ETL can help keep the storage costs low. ETL tools will transform and sift through the data to keep only the data you need. This helps decrease the use of data storage.

Here are a few disadvantages of ETL:

  • High Initial Cost: The initial cost of setting up your ETL process can be high as you need to define the Transformations and Processes you’ll be needing for your project.
  • Low Flexibility: If you leverage data sources and frequently changing formats for your business, you’ll have to configure the transformations for such edge cases and format changes well in advance. This is done to prevent modifications to the ETL process for every new use case. However, this can lead to a high maintenance cost.

Introduction to ELT

ELT became increasingly accepted as a viable alternative to ETL owing to:

  • The reduced cost and the recent popularity of Cloud-based Storage solutions.
  • The increased use of Unstructured data.

Here are a few advantages of ELT:

  • High Scalability: If the amount of data you use increases, you can quickly increase your storage in the Cloud. ELT processes can easily adapt to these situations and manage Data Ingestion at large scales. This is opposed to ETL processes that might have to be redefined.
  • Flexible: Since transformations don’t need to be described at the beginning, you can easily unify different and new data sources into the ELT process. 
  • Fast: You don’t need to stay idle waiting for the completion of the ELT process. Highly efficient ELT tools can load data instantly into your Data Warehouse where they’re ready for a transformation. 
  • Minimal Maintenance: As opposed to ETL, you will have less maintenance work on your hands since the ELT process is more automated and simpler to work with. Hence, it is easier to fix bugs in your transformation pipeline. This is because transformation is the last step in this process. But, you can only re-run the updated transformation to obtain the correct output.   

Here are a few disadvantages of ELT:

  • Data Security Risks: Data Security is a prime concern when loading large amounts of raw data into your storage. So, you’ll have to manage application and user access for raw data stored in your Data Warehouse to minimize security risks.  

Understanding the Differences between ETL and ELT

The main difference between ETL and ELT is that ETL transforms your data before loading it to the target Data Warehouse while ELT transforms the data only after loading it to the Data Warehouse.

Here are a few primary differences between ETL and ELT to help you make an educated guess:

ETL vs ELT: Compliance

ETL can rework and remove sensitive information before loading it into the Cloud Server or Data Warehouse. This simplifies compliance to HIPAA, CCPA, GDPR standards. ELT, on the other hand, needs you to upload the data before removing or reworking sensitive information. This means that it might violate HIPAA, CCPA, GDPR standards. This leaves sensitive information more vulnerable to inadvertent exposure and hacks. If your Cloud Server is based out of another country, it might transgress other compliance standards as well.  

ETL vs ELT: Maintenance Requirement

Cloud-based, Automated ETL solutions like Hevo, require little maintenance. Although an onsite ETL Setup that uses a physical server will need periodic upkeep. ELT, on the other hand, is Cloud-based. It generally houses automated solutions, so minimal maintenance is required. 

ETL vs ELT: Data Warehousing Support

ETL works with Onsite and Cloud-based Data Warehouses. The ETL Setup requires a Structured or Relational data format to function. ELT, on the other hand, works with Cloud-based Data Warehousing solutions to work with Unstructured, Structured, Semi-Structured, and Raw data types.    

ETL vs ELT: Unstructured Data Support

ETL can be used to Structure Unstructured data, but can’t be used to move unstructured data into the target system. ELT is a solution that can upload Unstructured data into a Data Lake. This allows Business Intelligence solutions to utilize Unstructured data.    

ETL vs ELT: Availability of Data in System

ETL only transforms and loads data that you deem necessary while creating the ETL process and Data Warehouse. This is the only information that will be available to you in an ETL setup. On the other hand, an ELT process can load your entire data almost instantly. It allows users to determine which data to transform and analyze at a later stage. 


This blog talks about a few of the primary differences between ETL and ELT in detail. It also gives a brief overview of the two processes, eliciting their benefits and limitations.


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