Chen Cuello
MAY 3, 2023
5 min read
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The times of running a business have drastically changed. The analysis methods we used a decade ago are incompetent to today’s technology. The old Excel Sheets are replaced with SaaS systems that make the analysis more demanding and accurate.

The ETL process in data warehousing is a specific process performed as a regular company workflow check. It is vital for the company’s growth and decision-making. However, these processes are demanding and challenging.

Below we define the ETL process, briefly explain the ETL data processing, and pay attention to one specific question—What are the steps of the ETL process? 

What is the ETL Process?

ETL modeling process refers to Extraction, Transformation, and Loading, but what is the ETL process, exactly? As the name implies, this process extracts information or data from source systems and transfers them into a data warehouse.

The process is delicate, requiring active or updated information from developers, testers, analysts, stakeholders, and top executives. ETL is the best method for a data warehouse system that will automate and neatly document the information daily, weekly, or monthly. There are two types of ELT processes: 

Below we discuss them in detail.

Traditional ETL Process vs. Data Warehouse ETL Process

The Traditional ETL approach requires a data scientist or analyst to develop the on-premise databases and data pipelines manually. As it’s a manual job, this way of conducting the ETL takes too much time to process. Plus, this type of technology is difficult to scale and evaluate and requires sacrificing raw data for the data volumes. 

Contrary to the Traditional ETL, there’s the Data Warehouse ELT. This one allows the transformation and data modeling in the SQL database, enabling all sides—data analysts, scientists, BI team, etc.—better control as they all understand the language.

In the following sections, we’ll review these ETL processes separately.

Traditional ETL

The traditional ETL method, as we mentioned, is manual; hence it is slower. All manual adjustments complicate the adaptation process in the business environment, so, as such, this model is recommended for relational databases that aren’t loaded with unstructured data. This is because engineers and developers will need more interaction if the ETL extracts more data from more sources. 

Additionally, if there is an increase in data, the traditional ETL requires more disk space to store all the information. Moreover, the calculations require fast processors to execute the operation successfully. 

So, if you sum up the costs for this type of ETL, you will realize that it’s time-consuming and costly. However, with today’s alternatives, it is very unlikely for a company to choose the traditional ETL process.

Data Warehouse ETL

With the technology we use every day, the data circing in a company doesn’t have only a single source of information. These sources could be enormous, making integrating them into your system difficult. What’s more, monitoring or managing this type of information is also problematic.

To increase your business performance, you need to incorporate a data warehouse architecture that can organize the data and building blocks and keep the data hygiene in order. Part of this architecture involves the Data Warehouse ETL tool.

As mentioned, this ETL process allows better control over the information gathered. Considering that data today is analyzed in a raw form, opposite to the previous preloaded OLAP summaries, the Warehouse ETL type is more suited, as it is more flexible and transparent.

How Does ETL Process in a Data Warehouse Work?

As you can see, both ETL types, although different, revolve around the same things—Extract, Transform, and Load. So, the next question that needs to be answered is, “What are the steps of the ETL process?” Below we explain the ETL process methodically.


The extraction refers to gathering processed and unprocessed data from various sources and storing it in a single archive. Unlike before, when information extraction came from a few sources, now the data inflow comes from many sources, including management data systems, social media, and Google Analytics.

This is the first step in the ETL process and is usually the most time-consuming. The main reason is that the data from the sources may be too complicated or otherwise difficult to process, hence taking more time to extract them. Another reason is that the gathered information may come in multiple formats. 


As the name implies, the second stage of the ETL process includes transforming the gathered data. There are several types of ETL transformation, which you can check in the link here.

But in general, two ETL transformation types are used: 

  • Data cleaning – It verifies the existing data and corrects the irregularities. The irrelevant data is dismissed or deleted. 
  • Data enriching – Checking if there are any missing parts in the data and filling them with new and correct information.

Out of the two, data cleaning is used more often today.


The final stage of the ETL process is loading the processed data. Specifically, it relocates the data from the second stage to a target database. This target system could be a data warehouse, a data lake, SQL, NoSQL, etc. Simply put, it is a place where it will be ready for major data analysis.

There are two main ways of data load: 

  • Full load – Loading the entire data at once, occurring the first time the data is loaded.
  • Incremental load – Loading data in certain intervals. Depending on the data type you’re loading, you can choose between streaming incremental load (for small volumes) and batch incremental load (for big volumes).

Top 5 Challenges in the ETL Process

Although a great way to accumulate data, the ETL process naturally comes with some challenges. The most common ones are the following:

  • Loss of data along the way;
  • Hard-to-read software requirements that slow down the process;
  • Difficulties in acquiring and creating test data;
  • Data quality during loading;
  • Scaling complexity.

Top 5 Benefits of the ETL Process

Every business, regardless of size, needs regular ETL checks to see improvements. It is essential for business growth because you can only expect an improved performance with full data insight. Essentially, the main benefits of the ETL process are the following:

  • Providing clear sight of the data;
  • Providing a development framework to simplify the decision-making process;
  • Advanced data profiling and filtration;
  • Correction of wrong information and clearing spam;
  • Improving overall performance.

What is the Difference Between ETL and ELT?

Although some use these terms interchangeably, ETL and ELT are different. Namely, the key difference is the order of the steps. In the ETL process, the data transformation happens before the loading phase, whereas the ELT process involves transforming the collected data after it’s loaded.

The ELT will load the collected raw data straight into a data warehouse instead of moving it to a processing server. Therefore, you can expect all data transformation, enrichment, and cleansing within the final stage. The ELT is a new data processing approach used to improve scalability. 

How Does Rivery Help in Automating Your ETL Process?

If you want to use or improve your ETL process, Rivery is here to help! 

Rivery is not only an extract and load tool but a complete end-to-end platform. We can offer not only ETL services but also data orchestration, transformation, and even reverse ETL. With the last one, you can quickly push data to where you want it, adopt operational analytics, and eliminate manual processes. 

We also offer ETLT processes with the industry’s first Python integration. 

Are you wondering how to use the ETL process or what type of ETL to use? Speak to a Rivery expert today, and see how you can employ the ETL process best!

Final Thoughts

Today, ETL is an essential process within any big business. As mentioned, regular ETL checks are crucial as they improve your insight into the current flow of information within the company and boost performance. 

However, with the daily influx of information, manual ETL data processing is difficult. This is where automated software comes into play. The market offers several options—all you need to do is choose the best ETL tool for you!

ETL Process FAQ

How does ETL help transfer data in and out of the data warehouse?

The ETL process uses ETL data pipelines to extract information from one source, transform it, and then load it onto another. Today, ETL tools take raw data to a staging area where cleaning, filtering, or other transformation is conducted and finally move it from the staging area into the target data warehouse.

Why is an effective ETL process essential to data warehousing?

The ETL process breaks down data collections, i.e., data silos, making the extracted data easily accessible to you. This way, you can analyze it better and incorporate it into your business. Essentially, this process lets you make better business decisions and scale.

Can RPA do the ETL process?

Technically, RPA can do the ETL process. Companies that want to integrate their business processes and data entirely often use them both. However, remember that ETL done by an RPA software is not recommended for processing raw information.

Is ETL automation possible? If yes – how?

Yes. ETL automation is possible and allows you to manage the ETL process through a simple point-and-click graphical interface. This type of tool doesn’t require manual coding, meaning you have more freedom to handle the influx of data and its transformation.

ETL automation tools may also offer built-in connectors that can help you get data from different sources, connect your existing system with a no-code solution, etc.

Minimize the firefighting.
Maximize ROI on pipelines.

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