Learn About Extract Transform Load (ETL)
with ETL Examples

Modern business is driven by data. Nearly every organization, regardless of industry, leverages data in a variety of ways to inform business decisions. This data comes from a wide range of sources — whether it’s data pertaining to customers, employees, vendors, products, or any other aspect of your business, there are likely dedicated data collection tools up to the task.

Collecting all that data, however, is only half the battle. Once businesses have gathered a large amount of data, the need for some way to organize and activate it typically becomes clear very quickly. 

What is ETL?

Extract-Transform-Load (frequently shortened to ETL) is a process of integrating data from multiple, separate sources into a single, unified target system, such as a data warehouse where the data becomes usable for whatever the business needs. 

Specific ETL examples vary widely because data can be applied in so many different ways to serve different business objectives. One application of the ETL process is to feed machine learning algorithms. 

Machine learning comprises a combination of complex models and algorithms that enable computer systems to learn from historical data and produce predicted outputs. The machine learning process relies heavily on reliable sources of clean data, which is exactly what the ETL process can provide. 

When implementing an ETL process, there are a few important things to keep in mind. First, you’ll most likely need to adopt a specific tool to facilitate the Extract-Transform-Load data integration process. 

You can find many examples of ETL tools online, and we’ll discuss a few different kinds later in this article. 

The second important thing to keep in mind is the relationship between ETL and SQL (Structured Query Language). In the case of most ETL examples, SQL commands are used to communicate with database management systems, so it can be useful to have some knowledge of ETL process SQL syntax.

The third useful point to remember is that the quality of your data depends on the quality of your ETL process. It can be worthwhile to have a plan in place for ETL testing to ensure your data is being successfully cleaned and loaded into the target system as intended.

Extract-Transform-Load data integration and its relationship with machine learning is a very complex topic, and we’ve only begun to scratch the surface. Let’s examine the ETL process in more detail and look at some ETL process examples.

What is the ETL process?

The ETL process can serve a variety of business purposes, such as transferring data out of legacy systems and cleansing data to improve its quality and verify its consistency. As its name suggests, the ETL process requires three distinct steps. 

Here is a brief overview of each of the three ETL process steps:

Extract

The first step is extraction. During the extract step of the ETL process, the data is moved or copied from various separate source locations to a staging database. Data may be extracted from a wide range of different sources and could include multiple different structured or unstructured formats.

Transform

The next step is transformation. During the transformation step, data is processed and reformatted as necessary so that it is compatible with the target system. There are multiple types of transformation. The type of transformation used depends on the requirements of the data’s target destination and the business’s intended application for the data. 

Load

Finally, the last step of the ETL process is loading. During the loading step, the data is transferred from the staging location to the target system. Usually, the target system is a data warehouse designed to house unified data sourced from disparate collection systems.

An effective ETL process begins with high-quality data collection methods. One of the best ways to ensure a smooth ETL process is to implement advanced data capture systems like Rossum’s intelligent document processing solutions. 

What is an ETL tutorial?

The data a business collects is not nearly as strategically valuable while it’s still locked away in individual silos. There needs to be a process in place to unlock the full potential of the data’s strategic value. This is where having a working understanding of ETL basics can be very useful.

If you want to improve your organization’s data analysis capabilities, one of the best places to start is with an ETL tutorial. You can find an ETL tutorial for beginners or ETL tutorial PDFs in many locations online. You can also find specific SQL ETL tutorials designed to help you learn how to use SQL commands to communicate with database tools. 

Which ETL tools are right for you?

An essential tool in data warehouse integration processes are ETL tools. A business’s ETL tools are what ultimately drive data activation by cleaning and unifying disorganized data gathered from disparate sources. 

One of the very first steps to implementing Extract-Transform-Load capabilities is choosing the right ETL tool (or ETL tools) for your organization. There are a few different kinds of ETL tools and many different applications for ETL processes, so you’ll most likely need to compare ETL tools and determine which type can best suit your business’s particular data needs. 

Let’s take a closer look at a few of the most popular ETL tools and their potential uses:

1. Enterprise Software Tools – Enterprise ETL software are extensive solutions with a full range of features that are targeted at very large organizations. These kinds of tools tend to offer very robust capabilities, but they can also be quite expensive and complicated to implement.
2. Cloud-based Tools – Many ETL solutions are also available as cloud-based tools. The advantage of a cloud-based ETL tool is that it introduces an added degree of accessibility and efficiency because the software is remotely hosted by the provider. This also means there is much less draw on internal resources. 
3. Open-source Tools – Open-source solutions are often free and provide direct access to the source code of the tool so you can tailor it exactly to your business’s needs. However, open-source ETL solutions also require a higher degree of technical knowledge to implement and upkeep since they come with no support and may include little documentation.
4. Custom Tools – Custom ETL tools present the best opportunity for a tool that provides the exact solutions your business needs because it’s built to your data team’s exact specifications. However, custom ETL tools are also generally the most resource-intensive to build. You’ll either need to commit the hours and resources to build the software internally, or agree to a relatively large price tag if you want to outsource the job.

What are some ETL tools examples?

With so many different types of ETL tools to choose from, including enterprise, custom, open-source, and cloud ETL tools, it can feel overwhelming to try to narrow down the list. How do you sort out the best ETL tools from the ones that simply aren’t right for your business?

Here are a few of the most important factors to consider when you’re evaluating different ETL tools for your business:

• How much data are you working with? One of the most crucial considerations is the scope of your business’s data integration and analysis needs. The amount of data you need to sort and activate will influence which tool is best. For example, the best ETL tools for big data companies may not be the same as the best ETL tools for smaller businesses. 
• What’s your budget? Another critical consideration is how much money your business has to spend on an ETL solution. 
• What’s your objective? There are many different specific applications for ETL data integration. Businesses can leverage the data they collect in countless ways to improve business outcomes. It’s important to make sure the ETL tool you select includes the particular capabilities you need in order to utilize your data in the way you intend.
• What types of data sources are you using? Different types of ETL tools may be set up to connect to different types of data sources. For example, whether your data is stored locally or in the cloud (in structured or unstructured formats) could have an effect on which kinds of ETL tools are best for your business.
• What is the technical literacy of your team? You also need to consider the capabilities of your in-house talent. If you have access to developers who understand how to manually code ETL solutions, a custom-built or open-source solution could work for you. If your team lacks the technical literacy to take an extremely hands-on approach, a third-party or automated solution might be a better choice. 

What are ETL transformations types with examples?

As the second of the ETL process steps, the transform step comes after the extract step of the ETL process. There are several different types of ETL transformations. Let’s take a closer look at some types of transformation in data warehouse integration processes.

What Are the Three Most Common Transformations in ETL Processes?

• Cleaning and validating data to ensure its quality and accuracy and removing any duplicated data.
• Translating, summarizing, or reformatting raw data, including converting currencies or units of measurement, performing calculations, or altering the format in which the data  is presented to match the format of the target data warehouse.
• Encrypting or otherwise protecting data to comply with applicable regulations.

What is en ETL process in data warehouse example?

Effective data extraction in data warehouse contexts is one of the most challenging aspects of data engineering. Verifying the quality of data is a slow and difficult process, and there are many opportunities for low-quality or duplicate data to slip through the cracks. 

The range of use cases in ETL process examples highlights the ability of an efficient ETL process to solve many of the challenges of organizing data in a reliable manner. 

ETL vs. ELT

The main difference between ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) processes is the order in which the steps occur. An ELT princess is generally better suited for processing large volumes of unstructured data because it simply loads the data from one source to another. 

The ETL process requires a bit more planning as it unifies data from disparate sources into a single, commonly-formatted database.