ETL PROCESS – An Easy Guide in 4 Points


ETL is an important component for data warehousing and analytics and is the process of transferring data from the source database to the data warehouse which is the final destination. Here, E stands for Extract, T stands for Transform and L stands for Load. Using the extraction process the data is first extracted from the source database, then the data is transformed into the format which is required and finally, this required data is loaded into the destination warehouse.

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Therefore, ETL can be defined as the process by which data is extracted from the source database, then transformed into the required format (like applying calculations, concatenations or anything else) and lastly the data is loaded into the Data Warehouse system which is the final destination.


As we know, ETL is a three-step process to carry the integration of data from the source to the destination with the help of some ETL tools about which we will discuss later. The three steps are as follows-

  • Extraction- In the very first step, the data is extracted from various sources out there in different formats and is then collected together in an area called the staging area where the data is rectified or sorted out. There may be instances where the data can be virus affected or fully corrupt and can ruin the whole functioning of the warehouse.

      Data extraction can be done in three ways which are: Full Extraction where the entire data can be reloaded to get it out from the system, Update Modification where the system itself alerts or notifies you when the data has been changed and lastly Incremental Extraction where you can identify the modification and according to that data can be drawn or extracted.

  • Transformation- In the second step, the data which was collected in the staging area and were sorted out are now transformed or converted according to the desired format because raw data which is full of errors cannot be direct to the Warehouse system. 

      The process of transformation of data can be of two types which are: The first one is the Basic Transformation Process in which the data as we learnt earlier is simply extracted, then transformed and then loaded in the Data Warehouse system and the second one is the In-warehouse Transformation in which the data which is extracted from various sources is collected in the staging area from where it gets loaded to the Data Warehouse directly. After reaching the final destination which is the Data Warehouse the process of transformation is carried out.

  • Loading- The third and the final step is that of the Loading of data to the final destination which is the Data Warehouse after being properly extracted and transformed into the desired format. This is a very crucial step and must be totally optimized for smooth functioning. 

     There is two processes for smooth loading which are: Full Load where the entire data is selected rather dumped into the Data Warehouse. Although it is not the best process to go for as it requires a lot of time and a lot of labour and the second one is Increment Load were unlike the Full Load, the data here is loaded in short intervals or breaks.


ETL tools are the ones which help us to extract, transform and load data from the source to the final destination which is the Warehouse smoothly. There can be numerous ETL tools but the most suitable tools may vary according to the situation one is in. Some of the best ETL tools for 2021 are –

  • Xplenty- It is a data integration platform which is cloud-based and offers to build data pipelines between a different source and its destinations. It also provides a few more advantages which are security, scalability and efficient customer service.
  • Stitch- It is a data integration platform which is an open-source ETL. It is appropriate for comparatively more advanced use cases and a larger number of data sources because it offers paid-service tiers
  • Talend- It is yet another open-source data integration platform which can handle data sources both in the cloud and on-premises. It also consists of hundreds of pre-built integrations.
  • Informatatica PowerCentre- This feature-rich data integration platform is known for its high performance and its capabilities with both SQL and non-SQL databases.
  • Oracle Data Integrator- It is a part of Oracle’s data management ecosystem and is very comprehensive in nature. It consists of both on-premises and cloud versions.
  • Fivetran- It is a data integration platform with a very wide array of different data sources. Also, it is a cloud-based ETL.
  • Skyvia- It allows big data integrations as compared to the others and also consists of migration and backup. It also consists of a cloud platform.

         There are a few more other tools of ETL to be considered as well and they are Striim (real-time data integration platform), Matillion (cloud ETL platform), Pentaho (open-source ETL platform), AWS Glue (end-to-end ETL offering), Panoply (self-service cloud data), Alooma (cloud ETL platform), Hevo Data (cloud database) and lastly, FlyData (real-time data replication platform).


Some of the examples of ETL are: 

  • Data warehousing where the combination of historical and current data from various sources is formed to develop a data warehouse. 
  • In Data Migration projects ETL is widely used when the data needs to migrate from the source to the destination.
  • In Data Integration during Merger process ETL is widely used when the big organizations are forming a merger with small firms and the data needs to integrate from one organization to another.
  • In the Third-Party Data Management ETL is essential. In big organizations, one or two vendors can not manage all the work and therefore the interference of the third party is required.


The most important aspect of every single business is their data and to arrange or sort out this data ETL process a very significant part of data warehousing projects. The ETL process must be accepted by all the big organizations especially to handle or deal with bulk data.

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