The ETL process is important in the overall process of data integration and for various other strategies. It enables users and companies to collect data from different sources and manage it in one central place. ETL is also known for making different types of data work together in a similar way. 

In a normal process, different types of data are combined and processed and then stored in a specified location. The locations are widely known as Databases known. In addition, the ETL process can also allow data to be moved across a variety of sources. Data can also be migrated across different analysis tools and destinations at the user's discretion. 

Functionality of the ETL process

There are three steps involved in the whole process. These steps facilitate the consolidation of data from source to destination. They are: 

  • Extract 
  • Convert 
  • Save 


The first step in data management is always to extract the data from the source. The extraction process must be done before moving the data to a specific location. Both structured and unstructured data are combined here. Data sources can be, for example: 

  • Analysis tools 
  • CRM systems 
  • Mobile phone applications 
  • Legacy and database systems 
  • Storage platforms 


In the second phase, the ETL process transforms the data. Here you can set up rules and regulations to control access to and the Data quality to help your company comply with the reporting standards. 

The conversion process can be broken down into various sub-processes. These include: 

  • Deduplication: Here, all repetitive data is sorted out and finally discarded. 
  • Cleansing: The clean-up corrects all missing values and irregularities and cleans the data. 
  • Check: Here all data in the system are checked again for correctness. If there are incorrect values, they are corrected or even removed. 
  • Standardisation: The formatting rule is applied so that all data in the system is at a similar level. 
  • Sorting: Organisation of data by type. 


As the name of the process suggests, the final step of the ETL process is to store the already converted data in a selected location. There are two ways to store the data: 

  • Save all data at once 
  • Save data step by step 

Save all data at once 

Here, all converted data in the database is transformed into unique and new records. However, this type of data storage can be difficult, especially because of the large number of records that are stored all at once. 

Save data step by step 

In this process, new data is gradually compared with the data already available. New records are only created if the data is unique and new. Saving data step by step is somewhat easier than saving all the data at once.