image showing how the data flows from sources to analytics tools

ETL Process: What It Is & How It Works

The ETL process is a widespread data integration strategy whose popularity has increased with the growth of data-driven applications. Starting in the 1980s, when data warehouses took center stage, the first ETL tools appeared to help early adopters move data from one or more sources into a staging area to transform it in some way, make it available, and do it repeatedly.

But the cloud changed everything forever. Do we still need ETL? Is it still important? The answer is “yes”. Although it might not be the traditional ETL process and we might not need dedicated ETL hardware before loading data into the warehouse, most data-driven companies today use some form of ETL strategy (even if they don’t call it that way). This article will explain what the ETL process is, why is it important, and what a modern ETL (or ELT) process looks like.

What Is ETL?

ETL stands for “extract, transform, load” and it describes the three processes that work together to consolidate data into a single, centralized location, typically a database:

  1. Extract the data from its source, whether that is a database or an application.
  2. Transform the data to ensure quality by cleaning it up, deduplicating it, combining it, etc.
  3. Load the data into the target database.
ETL process diagram

Why Is the ETL Process Important?

The ETL process is important because it plays a critical role in executing broader data management strategies and providing a consolidated view of data for easier analysis and reporting. 

Business Benefits

Beyond extracting, transforming, and delivering data, ETL has several business benefits. From streamlining repeatable processes and making data more accessible and easier to act on to improving accuracy for low-risk decision-making and better compliance.

Consolidates Data

Even with cloud computing entering the space, data still needs to be brought to a central repository—now from more sources than ever, in structured and semi-structured forms. Data in its raw form is not valuable. Those huge stores of data need to be collected and transformed into formats suited for analysis.

Prepares Data for Analysis

ETL makes it possible for different types of data to work together, collects and refines different types of data, delivers the data to a target destination, and makes it possible to migrate data between a variety of sources, destinations, and analysis tools to drive insights from it.

What Are the Steps of a Traditional ETL Process?

If you’ve come across ETL, you may have asked: “What is extraction, transformation, and loading in data management?” Here’s a breakdown of the ETL process meaning and how each of the three steps contributes to collecting and refining different types of data. All three phases serve a specific purpose to move, protect, and support your information so that it becomes functional and reliable.

1. Extract

Most companies manage complex and valuable data scattered across numerous sources and presented in various formats. To execute their data strategy, they must pull the raw data from its source to consolidate it into a single repository. This is the first step of the ETL process.

Data can be extracted from all kinds of sources or applications, including:

  • Existing databases or data warehouses
  • Cloud or on-premise environments (or hybrid environments)
  • Customer relationship management (CRM) tools
  • Enterprise resource planning (ERP) data
  • Sales and marketing applications
  • Internet of Things (IoT) sensors
  • Mobiles devices and/or apps
  • Any other data storage platforms or analytics tool

The extraction step is essential to take and combine the data that needs transformation to be useful in data analysis. That data is generally pulled into a staging area that sits between the data source and the target destination. Although this process can be hard-corded by a team of data engineers, ETL tools use automation to create a time-efficient and reliable workflow.

2. Transform

The second step in the ETL process is data transformation. During this phase, the goal is to ensure that the data retains its quality and matches the needs of the organization by setting certain rules. These qualifications protect the quality of the data while making sure that it is useful and accessible to the organization. 

In the traditional ETL process, transformations occur in an intermediary staging area, like an ETL server. With cloud computing, this step is completely modernized to take advantage of the full capabilities of the cloud (more on this in the next section).   

The main transformation processes include:

  • Cleansing: Removes inconsistencies or missing values.
  • Standardization: Uses formatting rules to make the data set consistent.
  • Deduplication: Removes duplicates or other redundant data.
  • Verification: Removes unusable data and flags anomalies.
  • Sorting: Organizes data in a data set by type.

However, there may be additional tasks or customized rules companies set in place to improve the quality of their data and help meet reporting requirements.

3. Load

The final step in the ETL process is to load the extracted and transformed data into the target destination, typically a database. There are two types of data loading:

Full loading

In the full loading method, all the data that comes from a transformation batch goes into the target destination as new, unique records. This process is fairly easy to implement and it doesn’t require monitoring whether or not all the data is up to date every time you reload the table. However, full loading is unsustainable in the long run because the datasets grow exponentially and can become difficult to maintain.

Incremental loading

The incremental loading approach is more manageable and allows for faster processing. In this method, the system compares incoming data with what’s already in the target destination, and only creates additional records if new and unique data is found. Yet, incremental data loads present challenges too. The constant movement of data requires monitoring for errors and potential incompatibility and sequencing issues.


ELT (extract, load, transform) is similar to but different from ETL processes. While most traditional ETL software extracts and transforms data before ever loading it into a database, ELT extracts and loads the data first, then transforms it. With ELT, there is no need to clean the data on specialized ETL hardware before putting it into a data warehouse. Instead, ELT creates a “push-down” architecture to take advantage of the native capabilities of the cloud:

  1. Extract the data from multiple sources and connectors
  2. Load it into the cloud data warehouse as-is
  3. Transform it using the power and scalability of the target cloud platform

A traditional data warehouse architecture cannot expand, at least not quickly or cost-effectively, to keep and handle the volume of data we generate and collect today. The cloud is significantly more scalable in terms of storage and processing. However, traditional ETL is unlikely to benefit from the best practices and inherent benefits that a cloud data warehouse provides.

On the contrary, cloud-native ELT makes use of the best aspects of a cloud data warehouse, including scalability as needed, massively parallel processing of multiple jobs at once, and rapid job spin-up and tear-down. Traditional ETL is effective if you are still on-premises and your data is small and predictable. However, as more companies choose a cloud- or hybrid-based data architecture, that is less and less the case. ELT is the future.

ETL vs ELT diagram

What You Should Look for in an ETL/ELT Tool

Modern ETL/ELT tools should automate the extraction, transformation, and loading process so that companies can focus on business value and eliminate outdated, labor-intensive, and time-consuming practices like hand-coding. A reliable platform will have data profiling, data cleaning, and metadata-writing capabilities to ensure data is consistent and usable. Along with its data processing capabilities, a tool should also be:

  • Intuitive and easy to use so that your team can quickly access and understand data
  • Easy to maintain so that you aren’t dealing with new problems instead of solving current ones
  • Compatible with your current data solutions (and future ones as your company grows and adapts to modern data architectures)

Your company can hire developers to build an ETL process, which is an expensive and long process. That’s why most people opt for an ETL/ELT tool that can do all of the above—those data processing solutions are more cost-effective, fast, and more applicable across a variety of data management strategies.

Final Thoughts About the ETL Process and Next Steps

The ETL process is a key part of data warehousing and making the most of your information, both for analytics and processing. Learn more about ETL for API data here or, if you really want to utilize your data, check out Ascend’s data automation cloud platform, which can revolutionize your engineering, analytics, and business practices.

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Additional Reading and Resources