What Is ETL?

Does any job field have as many acronyms as data engineering? There are DBA (database administrator), ODS (operational data store), RDBMS (relational database management system) and hundreds of other examples! ETL (Extract, Transfer and Load) is perhaps one of the most important ones. That’s because it’s still the most-used data integration method among engineers. 

In this glossary, learn the answer to the questions “What is ETL?” and “how does ETL work?” discover the benefits and drawbacks of ETL and read about real-life examples of this data integration strategy. 

ETL’s Meaning Defined

ETL stands for Extract, Transform, Load. This three-letter acronym refers to a data integration method that moves data from different—sometimes disconnected or “disparate”—data sources to a centralized target system. It’s a lot simpler than it sounds.

The primary goal of ETL is data analysis. Once an engineer moves data to a single system, it’s easier to run data through business intelligence (BI) tools and identify patterns and trends that improve decision-making. Data scientists get more value from data and become better at their jobs!

ETL isn’t the only data integration method. Engineers can integrate data via:

  • ELT (Extract, Load, Transform), Reverse ETL
  • Change Data Capture (CDC)
  • And other processes


However, ETL remains the most popular method because it offers speed and cost-effectiveness. (Businesses that want to integrate data love speed and cost-effectiveness!) 

ETL dates back to the 1970s when databases were becoming more popular. This method has evolved since then, and now ETL tools automate much of the data integration process—an incredible thing for engineers! 

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ETL Meaning: How Does ETL Work?

Each letter in “ETL: represents a different technique:

  1. You start by extracting data from a data source such as a database, SaaS application, customer relationship management (CRM) system or enterprise resource planning (ERP) system. You place this data in a staging area. So far, so simple. 
  2. Then, you transform that data into the correct format for data analysis. You might also clean the data, removing inaccuracies and duplicate data sets and ensure data adheres to legislation such as GDPR and HIPAA. (That prevents organizations from paying expensive fines for not complying with data protection frameworks.)
  3. After transforming data, you load it to a centralized target system such as a data warehouse. From this point, you can run data through BI tools and data scientists can generate as many insights as they like!


The above process requires data engineers to build big data pipelines that facilitate data movement from one location to another. That requires lots of coding experience—pipelines can take days or weeks to complete, which sucks. Modern tools automate the ETL process by providing no-code/low-code connectors that move data from a data source to a target destination with minimal engineering.  

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What are the Benefits?

When searching “ETL define” and “ETL meaning” in Google, you might come across many benefits this data integration method can offer. Here are some of the most important ones:

Single Source of Truth

ETL provides a single source of truth for all the data in an organization, allowing them to view data insights in one place. There’s no need to use multiple systems for data management and analysis. 

Say a company uses 20 different software tools in their organization. It would be difficult to compare data sets from each tool without integrating that data and moving it to a centralized system. ETL helps the organization do that. 

Business Intelligence

Moving data to a target system like a warehouse and running it through BI tools can provide organizations with business intelligence about customers, inventory, sales, marketing and day-to-day processes. 

Say a company wants to identify customers interested in its products and services. ETLing that data can provide the company with valuable customer insights for future marketing campaigns. 

Removes Data Silos

Data silos are data repositories belonging to a business department that are isolated from other repositories, making it difficult to compare data sets and improve decision-making. Moving data to a target system can remove silos and result in more effective data analysis. 

What are the Drawbacks?

Here are some of the negative points of ETL:

Not Real-Time

Traditional ETL relies on batch processing, which processes data at regular intervals. Although users can generate insights from ETL, these insights aren’t available in real time, which might influence decision-making. Streaming ETL (or real-time ETL) is an alternative to traditional ETL that uses stream processing to generate real-time intelligence. 

ETL Tools Won’t Cover Every Scenario

As previously mentioned, ETL tools use low-code/no-code connectors to automate the ETL process, reducing the need for engineers to build complex big data pipelines. However, engineers will still need to create pipelines for ETL workflows not served by these connectors. (Qualified and talented engineers will relish this opportunity!)

Take Your Data Skills to the Next Level

There has never been a better time to improve your data skills. The Data Incubator offers an immersive data science bootcamp where data science experts teach you the skills you need to succeed in the world of data. 

Here are some of the programs we offer to help you turn your dreams into reality:

  • Data Science Essentials: This program is perfect for you if you want to augment your current skills and expand your experience. 
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  • Data engineering bootcamp: This program helps you master the skills necessary to effortlessly maintain data, design better data models, and create data infrastructures. 

We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.


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