ETL Process: Extract, Transform & Load Explained

ETL Process

In the realm of data management, where ETL stands for Extract, Transform, & Load, which is an essential part of the data warehousing process, it allows organizations to gather data from various sources for their convenience. 

But with this, everyone is very interested in knowing how the ETL process actually works. So, to let them know about this, we are here with this blog, where they will delve into the intricacies of each phase of the process and explain its significance.

Extract: Gathering of Data

This is the first stage of the process that is carried out on the ETL platform. This involves retrieving data from multiple source systems, such as online services, databases, cloud storage, flat files, and more. As a result, gathering the data in its original format without changing it is the phase’s main responsibility. This is due to the fact that everyone would have numerous issues, such as difficulty finding items conveniently for processing, if modifications were made without consultation or even notice.

Not only this, the step is crucial, as if the data isn’t properly extracted, then how will the other phases involved in processing take place? So, let’s understand this level in detail:

Types of Data Sources

  • Relational Databases: This is the type that structures the data that is stored in tables in the form of rows & columns.
  • Non-Relational Databases: In this type, data is stored in a more flexible format, which makes it more convenient for the ETL tools to perform their function.
  • Flat Files: This contains those simple files like CSV or TXT that contain raw data.
  • APIs & Web Services: This contains those data files that are retrieved from various online sources & even external systems.
  • Cloud Storage: Here, data gets stored on cloud platforms like AWS, Azure, or Google Cloud.

Challenges of Data Extraction

  • Not Knowing Well About Data Heterogeneity: when no one is fully aware of the fact that data is even stored from various sources in different formats & structures.
  • Data Volume: This will help in extracting a large volume of data from various places, which is mainly time-consuming and resource-intensive.
  • Not Understanding Data Quality: We need to ensure data quality because maintaining accuracy and consistency of the extracted data is critical.

Techniques Everyone Can Use for Data Extraction

  • Full Extraction: Through this, the various data ETL tools can easily extract the whole data in one go.
  • Incremental Extraction: This method would lessen the strain on the source system by allowing users to extract just the data that has changed since the last extraction.

Transform: Converting Data into a Usable Format

This is the phase that starts working at that time, once the data is extracted. The step involves cleaning, enriching, and transforming the data to fit the desired format for analysis or reporting. Other than this, the steps do ensure that the data is accurate, consistent, and useful. With this, there are many different things involved in this step, let’s have a look at them:

Key Transformation Processes

  • Data Cleaning: This process does result in cleaning that data, which is once extracted from various sources & removes duplicates, corrects errors, as well as handles missing values.
  • Data Standardization: After the cleaning of data, this process converts the same into a better and more standard format or structure, which can be used more conveniently in an emergency.
  • Data Enrichment: Enrichment means making the data more meaningful, for which additional information gets added to the information in order to enhance it’s value.
  • Data Aggregation: This helps everyone by summarizing the data, which, as a result, will provide a high-level view.
  • Data Filtering: Filtering mainly helps in sorting the data to remove irrelevant or unnecessary data.
  • Data Integration: Here, data from various & multiple sources gets combined into a single, cohesive dataset.

Common Transformation Techniques

  • Mapping: This mostly involves linking various data fields from the source system to the target system.
  • Joining: At this point, data from different locations gets merged just based on a common key.
  • Sorting: Sorts & arranges the data in an easy format for convenient analysis.
  • Derivation: Adding new data fields to the existing ones.

Loading: Storing the Transformed Data

This is the last and most crucial stage of the ETL process, where the transformed data is finally fed into the target system—a data lake, data warehouse, or other kind of database—of the process. Making the data available for business intelligence, reporting, and analytics is why it’s such an important step.

For the loading of data, mainly different techniques are used. So, let’s know about them:

Techniques for Data Loading

  • Batch Loading: This technique loads the large batch of data at scheduled intervals into a form that can help everyone.
  • Real-time Loading: This loads the data at the time of receiving only by enabling real-time analytics.


The details of the ETL process—Extract, Transform, and Load—are a cornerstone of modern data management strategies. So, before performing it, you should have a look at this detailed overview, as this will help you convert the data into a usable format that can be loaded into a target system.

However, if you still have questions or need more assistance, please don’t hesitate to contact us and our knowledgeable staff. We’re always here to help you in any way we can.

Also. You Can Read Blog on ETL vs ELT


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