What is ETL? Extract, Transform, Load Fully Explained

We know you've probably stumbled across the term “ELT” or “ETL” in your quest to build a data-driven organization. In this blog post, we'll explain what ETL is and why it matters for data integration. The acronym “ETL” stands for the data integration method of Extracting, Transforming, and Loading data from multiple sources (CRMs, emails, SaaS, ERPs) into a unified storage system, which can be a database or storage tools and architectures like a Data Warehouse.

Data Integration

Before we dive into what ETL is and its use cases, it's important to understand the main data integration methods out there and what they actually mean for companies chasing the dream of becoming data-driven.

Is your company data-driven? Find out in this article! 😎

You probably know the drill: within an organization, multiple different systems are used to help manage goals, processes, customers, employees, finance, and results. But how connected is all this data really? Can the sales team link their data with finance? Is sales performance effectively tracked not just within sales, but also by HR?

It's when we think about this connection that the need to integrate this data arises, so it can be useful for everyone. Simply put, this process combines data collected from all organizational sources into a single environment, providing integrated access to all users who need it and driving more valuable insights.

So, what is ETL?

ETL is a way to gather, clean, catalog, and store all of an organization's relevant data in a single environment, making it easy to access for all key stakeholders and connecting it globally to support action and decision-making. This process isn't new; it has been used for decades by all kinds of companies and organizations.

However, with the ever-growing volume of data circulating globally and the need to access, clean, and interpret it quickly, these technologies have undergone significant modernization and transformation, such as cloud storage. To move forward, it's important to understand what each letter of the acronym stands for and how the process impacts data preparation:

Extract

As we explained above, most organizations use various systems and software that generate critical data for each business area, and the ideal scenario is for them to communicate with one another. Because of this, the extraction process is about exporting, copying, retrieving, or simply pulling data from all its sources, whether structured or unstructured.

Some data sources include:

• SQL servers;
• Spreadsheets, emails, and HTML web pages;
• CRM software, ERP systems;
• Payment processing systems;
• Social media, like Instagram and LinkedIn;
• Ad platforms;
• Email marketing systems, like Active Campaign and MailChimp;
• NoSQL databases like MongoDB.

The extraction process pulls all data from various sources into a single system, but it doesn't clean or convert them into any standard format yet, this stage is purely focused on data acquisition!

Transform

Transforming the raw data extracted from the sources mentioned above serves to map, process, and convert it from its original format into a consolidated one, so it can be stored and easily queried and used in the future. Some tasks performed in this process include:

• Cleaning and deduplicating data;
• Auditing extracted information to ensure the accuracy, consistency, and reliability of the data;
• Applying encryption, passwords, or security layers to confidential or sensitive data;
• Formatting data according to the target storage system.

Load

In this step, ETL loads the transformed data into the storage structure used by the organization. This can happen by loading all data at once, via scheduled batch updates, or through incremental loads. This process can be completely automated.

ETL vs ELT: What are the differences between them?

Now that we understand each step of the acronym, let's explore another operational order for this process: ELT. Both processes achieve the same goal: unifying and integrating data from multiple sources into a single system, directory, or architecture through a data preparation and staging pipeline. However, there are two fundamental differences in their implementation: where the data is transformed and when that transformation happens.

In ETL, data is extracted, transformed, and loaded, in that order. In ELT, the loading process happens before transformation, meaning data is extracted, loaded into the storage system, and then transformed as needed.

ETL

• In organizations with strict privacy and compliance requirements regarding data confidentiality, sensitive information can be masked or omitted before being loaded into the storage system.
• In this case, data analysis can be done more consistently since the data is already structured and ready for the user.

ELT

• In ELT, data is typically transformed directly inside the company's analytical database.
• Since data processing happens on-demand as needed, there is greater speed and flexibility, allowing the organization to implement a system that combines characteristics of both a Data Warehouse and a Data Lake, also known as a Data Lakehouse.

Conclusion

In today's world, for your organization to stay competitive, it needs to generate and know how to leverage its data, regardless of your profile, industry, or corporate structure.

The larger the organization, the more departments are managed through different systems like SaaS, CRMs, and ERPs, creating an organizational challenge to properly leverage this information, as data easily gets lost and siloed within each department.

This is why data integration is crucial. Data-driven organizations guide their strategies and actions based on information that provides a complete, holistic view, rather than relying on fragmented data from isolated departments.

In this context, ETL and ELT are great data integration methods, extracting and loading data from scattered sources into a single repository like a Data Warehouse, Data Lake, or Data Lakehouse. To learn more about building a Data-Driven organization, visit the Erathos blog and stay up to date with the most important trends in the data revolution.

Read also: Data Analytics: 3 steps to start your startup's data-driven journey – with or without a data team.

Want to know how we can help your company? Reach out to us!