What is ETL?

We know you’ve probably already come across the terms “ELT” or “ETL” while researching how to build a data-driven organization. In this blog, we’ll explain what ETL is and why it is important for data integration. The acronym “ETL” is used to describe the data integration method that consists of Extracting, Transforming, and Loading data from multiple sources (CRMs, emails, SaaS, ERPs) into a unified storage system, which can be a database or even storage tools and architectures, such as a Data Warehouse.

Data integration

Before we dive into what ETL is and its applications, it’s important to understand the main existing data integration methods and what this means in practice for companies pursuing the dream of becoming data-driven.

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

You probably know this scenario well: within an organization, several different systems are used to help manage goals, processes, customers, employees, finances, and results—but how connected is all this data really? Can that organization’s sales team truly connect with finance, and are sales reps’ results tracked effectively not only by sales, but also by HR?

And when we think about this connection, the need arises to integrate this data in a way that makes it useful for everyone. Put simply, this process combines data acquired from all organizational sources into a single environment, enabling integrated access for all users who need it and generating more useful insights.

So, what is ETL?

ETL is a way to gather, process, catalog, and store all relevant data from an organization in a single environment, making it easy to access as needed by all key stakeholders and connecting it globally, helping support actions and decision-making. This process is not new and has been used for decades by many different types of companies and organizations.

However, with an ever-growing volume of data circulating worldwide and the need to access, process, and interpret it quickly, these technologies have undergone significant modernization and transformation, such as cloud storage. To continue, it’s important to understand the meaning of each letter in this acronym and how the process impacts information processing:

Extract

As we explained above, most organizations use various systems and software that individually generate important data for every business area, and the ideal scenario is for them to echo and intercommunicate. For this reason, the extraction process involves exporting, copying, retrieving, or simply extracting data from all its sources, whether structured or unstructured.

Some data sources are:

• SQL servers;

• Spreadsheets, emails, and web pages in HTML;

• CRM software, ERP systems;

• Payment processing systems;

• Social media, such as Instagram and LinkedIn;

• Advertising platforms;

• Email marketing systems, such as ActiveCampaign and MailChimp;

• Data from non-relational databases such as MongoDB.

The extraction process gathers all data from multiple sources into a single system, but it does not process or convert it into any standard format, since this stage is focused only on acquiring it!

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 are:

• Clean and unify duplicates;

• Audit extracted information to ensure the accuracy, consistency, and reliability of the data obtained;

• Apply encryption, passwords, or security layers for data that is confidential or sensitive;

• Formatting according to the data storage system used.

Load

In this stage, ETL loads the already transformed data into the storage structure used by the organization. This can happen by loading all data at once, with occasional changes and updates, or in partial increments. This process can be fully automated.

ETL vs ELT: What are the differences between the two?

Now that we understand what each letter in the acronym means, 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 processing structure. But there are two fundamental differences in their applications: where the data is transformed and when that transformation happens.

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

ETL

• In organizations with strong concerns about privacy and compliance for certain types of data confidentiality, sensitive information can be omitted before being loaded into the storage system.

• In this case, data analysis can be performed in a more stable way, since data is already available to the user in a structured format.

ELT

• In ELT, data is usually processed inside the company’s analytical database.

• With data processing happening according to usage needs, there is greater agility and flexibility for its use, allowing the organization to implement a system with characteristics of both a Data Warehouse and a Data Lake—that is, an analytical database—also known as a Data Lakehouse.

Conclusion

In today’s world, for your organization to stay relevant, it needs to generate and know how to use its data, regardless of its profile, segment, and corporate structure.

The larger the organization, the more departments are organized through different systems, such as SaaS, CRMs, and ERPs, and this creates an institutional challenge for making good use of this information, as it causes data to be lost and segmented within each department.

For this reason, having more integrated data is very important, since data-driven organizations guide their strategies and actions based on the use of information that provides a complete picture, and not just scattered data from each department.

In this context, ETL and ELT are good options for data integration methods, as they carry out the process of extracting and loading data from scattered sources into a single structure, such as a Data Warehouse, Data Lake, or even a Data Lakehouse. To learn more about ways to build a Data-Driven organization, visit the Erathos blog and stay up to date on the most important topics in the data revolution.

Also read: Data Analytics: 3 steps to start your startup’s data-driven journey—with or without a data team.

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