What does ETL stand for and what is it used for?

ETL stands for Extract, Transform, Load. It is the process that moves data from one or more source systems to an analytical destination, such as a Data Warehouse or a Power BI data model for reporting. How it is designed directly determines the quality and reliability of all the analyses and dashboards built on top of that data.

What is the difference between ETL and ELT?

In ETL (Extract, Transform, Load), data is transformed before being loaded into the destination. In ELT (Extract, Load, Transform), raw data is first loaded into the Data Warehouse and transformations are then applied using the DW's compute power. ELT is more efficient with cloud platforms like Snowflake or Azure Synapse, while ETL is more common in environments like Power Query within Power BI.

What is Power Query and how does it relate to ETL?

Power Query is the native ETL engine included in Power BI. It allows connecting to multiple data sources, applying transformations through a visual interface, and loading clean data into the Power BI data model — all without writing code. Each step is recorded in the M language, making the process reproducible and auditable. It is the most accessible ETL tool for mid-size businesses working primarily with Power BI.

Data Integration

What Is ETL: Extract, Transform, Load Explained for Businesses

By Juan Pedro Zingoni · February 5, 2026 · 5 min read

Every time a business tries to consolidate data from different systems to generate a report or a dashboard, it needs to solve a fundamental problem: how do you move that data from its source to the place where it will be analyzed? In most cases, the answer is called ETL. This article explains what it means, how each stage works, and what tools are available — from enterprise solutions to Power Query, the native ETL engine built into Power BI.

What Does ETL Stand For?

ETL stands for Extract, Transform, Load. It is the process that enables moving data from one or multiple source systems to an analytical destination, typically a Data Warehouse or a data model for reporting and business intelligence.

Although the concept dates back to the 1970s, its relevance has only grown with the exponential increase in data sources within modern organizations. Today, an ETL pipeline may pull from relational databases, APIs, Excel files, cloud services, CRMs, ERPs, and dozens of other sources. How it is designed largely determines the quality and reliability of the analysis produced at the end.

The Three Stages of ETL

Extract: Pulling Data from the Source

The first stage involves connecting to source systems and retrieving the relevant data. Those sources can be highly heterogeneous: a SQL Server database, a CSV file exported from an ERP, an Excel spreadsheet, a REST API from an external service, or even a real-time data feed.

The challenge at this stage is managing the diversity of formats, connection protocols, and update frequencies. A well-designed extraction is incremental: rather than downloading all data every time, it detects which records changed since the last extraction and only retrieves new or modified records. This reduces processing time and load on source systems.

Transform: Cleaning and Preparing the Data

Transformation is conceptually the richest stage of the process. This is where all the business rules required to convert raw data into analysis-ready information are applied. Common transformations include:

Standardizing date and text formats.
Removing duplicate records.
Normalizing values (for example, unifying "NY", "New York", and "New York City" into a single category).
Calculating derived fields (margins, percentage changes, running totals).
Joining or merging tables from different sources.
Validating referential integrity.

This stage is critical because the quality of analysis depends directly on the quality of the transformed data. A dashboard built on poorly transformed data can look visually polished and still lead to incorrect decisions.

Load: Writing Data to the Destination

The final stage consists of writing the transformed data to its destination: a Data Warehouse, a Data Mart, or directly into the Power BI semantic model. The load can be full (the entire table is replaced) or incremental (only new or updated records are added), depending on data volume and required refresh frequency.

Power Query: The Native ETL Engine in Power BI

For many mid-size businesses, the most accessible and powerful ETL tool is already included in their Power BI license: Power Query. Power Query is the data transformation engine that operates directly within Power BI Desktop and the Power BI service.

With Power Query, an analyst can connect to dozens of different data sources, apply transformations through a visual interface (without writing code), and load clean data into the Power BI data model. Each transformation step is recorded as an instruction in the M language, making the process reproducible, auditable, and easy to maintain.

Power Query supports the same operations as a traditional ETL process: combining tables from different sources, pivoting and unpivoting columns, filtering records, creating calculated columns, managing data types, and applying complex conditional logic — all without leaving the Power BI environment.

One of the key advantages of Power Query as an ETL tool is that every time the dataset is refreshed in Power BI, the transformation process runs automatically. This ensures dashboards always reflect the most current data according to the configured refresh schedule.

Enterprise ETL: Azure Data Factory, SSIS, and Other Tools

For organizations with more complex requirements — large data volumes, multiple critical sources, very low latency requirements, or pipelines that must orchestrate dozens of transformations — enterprise ETL tools are available.

Azure Data Factory (ADF) is Microsoft's cloud data integration service. It enables building visual data pipelines that connect cloud and on-premise sources, apply transformations with Data Flows (which internally use an interface similar to Power Query but at big data scale), and load results into Azure Synapse, Azure SQL Database, or other destinations. Its native integration with the Azure ecosystem makes it the natural complement to Power BI in enterprise architectures.

SQL Server Integration Services (SSIS) is the classic ETL tool in the Microsoft on-premise ecosystem. While still relevant, many organizations are migrating their SSIS pipelines to Azure Data Factory to take advantage of cloud scalability.

Other popular solutions include dbt (data build tool, widely adopted in modern architectures with Snowflake), Talend, Informatica, and Fivetran for extraction and loading.

ETL vs. ELT: What Is the Difference?

In recent years, with the rise of cloud Data Warehouses like Snowflake and Azure Synapse, a variant of the ETL process called ELT (Extract, Load, Transform) has gained popularity. The difference lies in the order: raw data is loaded into the DW first, and transformations are then applied inside the DW itself, leveraging its compute power.

This approach is especially efficient when the DW has massive processing capacity and simplifies the architecture by eliminating an intermediate transformation layer. Tools like dbt are designed specifically for this ELT pattern.

In practice, the choice between ETL and ELT depends on data volume, available platform, and team capabilities. Power Query follows the classic ETL pattern and is ideal for mid-size companies building their analytics primarily on Power BI.

Why a Well-Designed ETL Matters for Your Dashboards

Power BI dashboards are only as good as the data that feeds them. A well-designed ETL process ensures that data is consistent, current, and accurately reflects business reality. This directly impacts the trust teams place in reports and the speed at which they make decisions.

When ETL is poorly designed or fragile, dashboards fail during updates, show inconsistent data, or require constant manual corrections. A robust pipeline, on the other hand, turns data analysis into a systematic and reliable organizational capability. Cross-filtering in Power BI — where selecting a value in one visual instantly updates all others — only delivers meaningful insights when the underlying data has been properly transformed and modeled.

At Okun Data, we design ETL processes tailored to the size and maturity of each organization, from Power Query implementations in Power BI to enterprise pipelines with Azure Data Factory connected to a Data Warehouse in Azure Synapse or Snowflake.

Is your data taking too long to be ready for analysis?

We design ETL pipelines that automate your data integration and feed your Power BI dashboards with reliable, up-to-date information.

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Frequently Asked Questions

What does ETL stand for and what is it used for?: ETL stands for Extract, Transform, Load. It is the process that moves data from one or more source systems to an analytical destination, such as a Data Warehouse or a Power BI data model for reporting. How it is designed directly determines the quality and reliability of all the analyses and dashboards built on top of that data.
What is the difference between ETL and ELT?: In ETL (Extract, Transform, Load), data is transformed before being loaded into the destination. In ELT (Extract, Load, Transform), raw data is first loaded into the Data Warehouse and transformations are then applied using the DW's compute power. ELT is more efficient with cloud platforms like Snowflake or Azure Synapse, while ETL is more common in environments like Power Query within Power BI.
What is Power Query and how does it relate to ETL?: Power Query is the native ETL engine included in Power BI. It allows connecting to multiple data sources, applying transformations through a visual interface, and loading clean data into the Power BI data model — all without writing code. Each step is recorded in the M language, making the process reproducible and auditable. It is the most accessible ETL tool for mid-size businesses working primarily with Power BI.