ETL (Extract, Transform, Load): what it is, how it works, and the difference from ELT

Every report the board looks at, every BI dashboard, and every dataset that feeds an artificial intelligence model depends on a silent process almost nobody sees: ETL. It is what takes data scattered across several systems, tidies it up, and delivers it in one place, ready to use. When ETL works well, nobody notices. When it works badly, the company decides with wrong numbers and takes months to find out why. In this guide I explain what ETL is, how it works in practice, what the difference from ELT is, who takes care of it, which tools exist, and why none of this replaces the governance of your master data.

What ETL is

ETL stands for Extract, Transform, Load. It is the process that moves data from one or several source systems, applies cleansing and standardization rules, and writes the result to a destination, usually a data warehouse or a data lake, where the information will be analyzed. Put directly, ETL is the bridge between the place where data is born, such as the ERP, the CRM, or an area's spreadsheet, and the place where data is used to decide.

Translated to the company's day to day: finance has its data in the ERP, sales has it in the CRM, logistics has it in another system, and there is still that spreadsheet someone keeps on their computer. Nobody decides by looking at four screens at once. ETL is what brings all of that together into a single database, with the same standard, to become a report that adds up. Without it, the meeting turns into an argument about which spreadsheet is right, instead of a conversation about the business.

How ETL works in practice

The name already gives away the three steps. In extraction, the process connects to the source systems and pulls the data, which may come from the supplier master in SAP, from the invoices (notas fiscais) in the tax system, from CRM orders, and from finance entries. Each source speaks a different language, with its own formats and rules.

Transformation is where the heavy work happens and where most of the value lives. This is where the process standardizes the date format, adjusts the unit of measure, corrects text, applies business rules, and handles duplication. A classic example from the world of master records: the same supplier shows up as "Cia Industrial," "Cia Ind," and "CIA INDUSTRIAL LTDA" in different systems, and here you decide how to reconcile that so the purchasing report doesn't count three suppliers where there is only one. It is also in this step that you solve the problem where each area understands "customer" its own way and the number never matches.

The load writes the processed data to the destination. It can be full, when it rewrites the entire database, or incremental, when it updates only what changed since the last run, which keeps large databases running every day without bringing the system down. In the end, whoever consumes the data in BI receives information that is already clean and reliable, without needing to know where it came from.

ETL and ELT: what is the difference

ELT is the modern version of the same idea, with the order of the letters reversed: Extract, Load, Transform, that is, extract, load, and only then transform. The difference looks like a detail, but it changes the entire architecture. In ETL, the data is transformed before being loaded into the destination, generally on a dedicated server. In ELT, the raw data is loaded first and the transformation happens inside, using the processing power of the cloud data warehouse itself.

In practice, ELT was born along with the cloud. When the destination is a data warehouse platform that scales processing on demand, it makes sense to dump the raw data in and transform it afterward, with the flexibility to change the rules without reprocessing everything. Classic ETL remains strong in on-premises environments, with smaller volumes, sensitive data that needs to be handled before entering the destination, or complex business rules. It is not that one replaced the other. They are two models that coexist, and the choice depends on where your data is and where you want to take the company.

The right question, then, is not which is better, but which fits your context. ETL tends to weigh more when the environment is on-premises, the volume is moderate, or there is sensitive information to handle before loading. ELT tends to weigh more when the company is already in the cloud, the volume is growing fast, and the team wants agility to adjust transformations without reprocessing everything. What I see in companies is that this technical discussion often hides the real problem: there is no point choosing the most modern architecture if the source of the data is a mess.

Who takes care of ETL in the company

The professional who builds and maintains these processes is usually the data engineer, who designs the flows, connects the sources, writes the transformations, and makes sure the load runs every day. On the other side of the pipeline are the data analyst and the data scientist, who consume the finished dataset to generate reports, indicators, and models.

But there is a role almost always forgotten in this conversation. The business rule that the transformation applies doesn't come from technology. Whoever defines what an active customer is, what the correct record for a supplier is, or how to classify a material is not the data engineer, it is the business. That is the territory of the data owner and the data steward. When the company lets the engineer alone decide what each field means, it outsources to technology a decision that belongs to management, and then the report comes out technically perfect and conceptually wrong.

Which ETL and ELT tools exist in the market

The market has options for every scenario, and it is worth knowing the main ones neutrally. In the more traditional, corporate ETL field, solutions such as Informatica PowerCenter, IBM DataStage, Microsoft SSIS, Oracle Data Integrator, and SAP Data Services appear, along with platforms such as Talend and Pentaho, widely used in environments that mix legacy systems and complex integrations.

In the more modern field, oriented to the cloud and the ELT model, the ecosystem has grown a lot. Ingestion tools such as Fivetran and Airbyte make extraction and loading easier from dozens of ready-made sources, the major cloud providers offer services such as Azure Data Factory, AWS Glue, and Google Cloud Dataflow for orchestration, and dbt has become the reference for the transformation layer inside the warehouse. The choice of tool is important, but it is the last decision, not the first. Before it come the definition of the rules, responsibility for the data, and the standard the company wants to maintain.

ETL doesn't fix bad data: the link to master data

Here is the point I insist on most. ETL moves, joins, and organizes, but it doesn't decide which is the true version of data that is wrong or duplicated at the source. If the customer master has the same customer five times, ETL will load five. If the material is classified generically, ETL will carry that generic classification into the report. The rule has always held: bad data goes in, bad information comes out, only now it comes out faster and in more places.

That is why ETL and Master Data Management go together. MDM takes care of the single, trustworthy version of master data, the customer, the supplier, the material, the product, resolving duplication and defining the correct record before it spreads. ETL distributes that trustworthy version to the systems that need it. When the company invests in pipelines and forgets about master data, it builds an efficient conveyor belt carrying the wrong product. And this gets more serious with artificial intelligence on top, because a model trained on bad data doesn't correct the error, it only automates the wrong decision at scale. As I discuss in the book "Master Data Management: Fundamentos e Aplicações" (book in Portuguese), published by Editora Brasport, the foundation of any serious data project is the quality and governance of master data, and no integration tool replaces that.

How to learn ETL and data management

For those who want to enter this field, the path has an order that works. Start with SQL, the language that underpins practically any transformation and database check. Understand data modeling and the concepts of data warehouse and data lake, to know where the data ends up and why. Learn at least one tool end to end and practice building a real flow, even a small one.

But there is a part that separates the technical professional from the complete professional and tends to be neglected: the fundamentals of data governance and quality. Knowing how to move data is operational. Knowing how to guarantee that the data you move is the right data is what makes the difference in a career. This is where references such as DAMA's body of knowledge, the DAMA-DMBOK, and the CDMP certification come in. The engineer who masters the pipeline and also understands master data, quality, and responsibility for information is the professional companies have the most trouble finding.

Career opportunities

Demand for people who know how to work with data has been growing consistently, and data engineering is one of the most sought-after and best-paid roles in the field. The reason is simple: every BI, analytics, and artificial intelligence project depends on organized, available data, and someone needs to build and maintain that pipeline. There is no pretty dashboard or functioning AI model without a solid pipeline behind it.

The detail that opens doors is the one I already mentioned. The market has plenty of people who know how to build a pipeline and few who combine that technical skill with a governance and business view. A professional who understands procurement, supply chain, master record maintenance, or finance and learns to handle data becomes a rare bridge between the business area and the technical area. That is the profile growing fastest, because they don't just move the data, they understand what that data means for the company.

Main questions

Are ETL and ELT the same thing? No. Both move data from source systems to a destination, but the order changes. In ETL, the data is transformed before being loaded. In ELT, it is loaded raw and transformed afterward, inside the destination. ELT gained strength with the cloud and ETL remains strong in on-premises environments and with complex rules.

Do I need to know how to program to work with ETL? It helps a lot, mainly SQL, which is almost mandatory. There are visual tools that reduce the need for code, but understanding transformation logic and knowing how to check the result with SQL makes a difference in any scenario.

Is ETL useful for artificial intelligence? Yes, and it is essential. Every AI model needs prepared, clean, and available data, and the pipeline is what delivers that. But note: ETL doesn't correct bad data. If the source database is dirty, the AI will learn from the error. That is why governance and master data come first.

Does ETL solve data quality? It helps standardize and cleanse during transformation, but it doesn't solve it on its own. Data quality requires a defined business rule, a data owner, monitoring, and, in the case of master data, an MDM process that guarantees the single, correct version at the source.

What is the difference between ETL and data integration? Data integration is the broad concept of connecting and combining information from different systems. ETL is one way of doing integration, focused on extracting, transforming, and loading in batches. There are other approaches, such as real-time integration through APIs, which serve different needs.

MDM Academy

MDM Academy is the governance and master data school of 4MDG, created to train professionals the market needs and can't find. The proposal is to unite what usually comes separate: the technical knowledge of data and the vision of governance, quality, and business that turns that knowledge into results. The content starts from real company situations, from material and supplier masters to SAP projects and data preparation for artificial intelligence, always translating the concept into practical impact.

If you work with data, master record maintenance, procurement, supply chain, or technology and want to build a solid career in a field that only grows, it is worth getting to know MDM Academy and joining the Data Club, where we gather a community of professionals who discuss governance, master data, and the practices that really work inside companies.

Conclusion

ETL is the gear that makes data leave where it is born and arrive where it decides. Understanding the difference from ELT helps you choose the right architecture, but choosing the pipeline should never be the first conversation. Before it comes a more important question: is the data this pipeline will carry trustworthy? Because a modern pipeline carrying bad master data only delivers the problem faster, more efficiently, and on more screens. Technology moves the information. Governance ensures that the right information is what is being moved. And it is in that order, not the reverse, that good data projects begin.