ETL: Extract, Transform, Load — core concepts, use cases, and FAQs

Effective data management is essential for organizations looking to turn information into actionable insights. As businesses gather data from many different systems, organizing it into a structured format becomes critical for analysis and decision-making.

What is ETL?

ETL—short for Extract, Transform, Load—is a foundational data integration process that enables organizations to consolidate raw data, clean and reshape it, and load it into a centralized data warehouse. This allows teams to create a single source of truth for reporting, analytics, and downstream operations.

While ETL has long been the standard for organizing data pipelines, the rise of cloud computing and the explosion of unstructured data have led many teams to adopt a newer approach: ELT (Extract, Load, Transform). We’ll explore both methods in this post—along with the core steps, benefits, and use cases of ETL.

Who performs ETL?

ETL workflows are typically owned by analytics engineers or data engineers, depending on your team’s structure and the complexity of the pipeline.

When data is sourced from well-supported platforms—like Shopify, Stripe, or HubSpot—analytics engineers can often manage extraction using off-the-shelf data loading tools like Fivetran, Stitch, or Hightouch.

More complex or custom integrations usually fall to data engineers, who may need to script extraction jobs manually or deploy custom connectors. While many analytics engineers are technically capable of writing API integrations, building extraction pipelines is generally outside their core responsibilities.

In modern data stacks, low-code and no-code data integration tools are increasingly popular—even among data engineers—because they reduce the need for bespoke ETL scripts and improve maintainability.

Use cases: ETL in action

ETL is a foundational process for integrating and organizing data across industries. Here are some common use cases where ETL enables high-quality analysis and decision-making:

Retail

Retailers generate data from e-commerce platforms, POS systems, inventory tools, and loyalty programs. ETL pipelines help consolidate these sources into a centralized data warehouse—powering insights into sales trends, customer behavior, and inventory management.

Finance

Financial institutions use ETL to support risk management, regulatory compliance, and accurate reporting. ETL workflows extract transaction data, apply business rules (like fraud detection), and load cleansed data into centralized systems for auditing and oversight.

Healthcare

In healthcare, ETL helps unify patient records from EMRs, billing platforms, and departmental systems. Clean, integrated data ensures better reporting, care coordination, and operational efficiency across the organization.

Marketing and advertising

Marketing teams rely on ETL to unify data from ad platforms, CRMs, and email tools. By transforming raw campaign data into standardized metrics, they can build dashboards that track ROI and guide strategy in real time.

ETL vs. ELT: What's the difference?

As data volumes and complexity grow, traditional ETL workflows can hit limits around scalability and flexibility. That’s led many teams to adopt ELT (Extract, Load, Transform)—an approach that takes advantage of the performance and scalability of cloud-native data platforms.

Here’s a side-by-side comparison:

While ETL remains essential for specific use cases—like masking PII before data lands in a warehouse—ELT has become the default for most cloud-based data teams.

What is ELT?

ELT (Extract, Load, Transform) is a modern approach to data integration that flips the traditional ETL order. Instead of transforming data before it enters the warehouse, ELT pipelines load raw data directly into a cloud data platform—then apply transformations inside the warehouse.

This shift unlocks major advantages:

• Scalability: ELT leverages the computational power of cloud-native warehouses like Snowflake, BigQuery, and Redshift.
• Flexibility: Raw data can be loaded quickly, with transformations defined and versioned as code.
• Modularity: Teams can iterate on models over time—transforming data as new business questions arise.

It’s a software engineering-inspired approach that treats data pipelines like code, with all the benefits of CI/CD, testing, and documentation baked in.

When to use ETL vs. ELT

While ELT is now the default for most modern data teams, there are still cases where ETL makes sense.

Hybrid workflows: using ETL and ELT together

Many organizations adopt a hybrid approach—combining ETL for regulated or high-risk data, and ELT for scalable, iterative analytics workflows.

For example:

• Use ETL to hash or drop PII before raw data enters the warehouse.
• Use ELT to transform high-volume application data inside the warehouse using dbt models.

This hybrid model provides both control and agility—letting teams meet compliance needs without sacrificing velocity or flexibility.

What is the Extract, Transform, Load process?

In an ETL process, data is first extracted from a source, transformed, and then loaded into a target data platform. We’ll go into greater depth for all three steps below.

Extract

In this first step, data is extracted from different data sources such as databases, CRM systems, APIs, or flat files. These sources might contain structured, semi-structured, or unstructured data. Data that is extracted at this stage is likely going to be eventually used by end business users to make decisions. Some examples of these data sources include:

• Ad platforms (Facebook Ads, Google Ads, etc.)
• Backend application databases
• Sales CRMs
• And more!

To actually get this data, data engineers may write custom scripts that make Application Programming Interface (API) calls to extract all the relevant data. Data teams can also extract from these data sources with open source and Software as a Service (SaaS) products.

The data is consolidated into a staging area, where it is normalized and prepared for the next step in the process.

Transform

In the transformation phase, raw data is cleaned, transformed, and standardized. This step ensures that data is accurate, consistent, and in the correct format to be loaded into the target system.

Transformations can include filtering out bad data, removing duplicates, converting data types, applying calculations, and aggregating data. This is where business rules are applied to make the data useful for analysis.

To actually transform the data, there’s two primary methods teams will use:

• Custom solutions: In this solution, data teams (typically data engineers on the team), will write custom scripts and create automated pipelines to transform the data. Unlike ELT transformations that typically use SQL for modeling, ETL transformations are often written in other programming languages such as Python or Scala. Data engineers may leverage technologies such as Apache Spark or Hadoop at this point to help process large volumes of data.
• ETL products: There are ETL products that will extract, transform, and load your data in one platform. These tools often involve little to no code and instead use Graphical User Interfaces (GUI) to create pipelines and transformations.

Load

The final step is loading the transformed data into a data warehouse or data lake, where it becomes available for querying and reporting. Data can be loaded in batch processes or, in some cases, near real-time. Once loaded, business intelligence tools or data analysts can query the data for reporting and insights.

How ETL is being used

While ELT is increasingly common in cloud-native environments, ETL remains a valuable approach—especially in scenarios that require strong data governance or pre-load transformations.

Normalizing large data volumes

ETL can efficiently handle lightweight transformations at scale. For example, data teams might apply simple normalizations (like formatting date fields or unifying naming conventions) during the ETL process to make large datasets usable more quickly. This is especially helpful when business users need timely access to raw or semi-processed data in BI tools.

Hashing or masking PII before loading

ETL is often used to enforce data privacy policies by masking or removing personally identifiable information (PII) before data enters the warehouse. This reduces the risk of sensitive data exposure and helps organizations meet compliance requirements by restricting where and how PII is stored.

Benefits of ETL

ETL offers significant advantages for organizations focused on data quality, governance, and performance. Here are some of the key benefits:

Improved data quality and consistency

ETL applies validation and standardization rules before data reaches the warehouse. This ensures that downstream users work with consistent, reliable datasets—regardless of the original data source.

Early application of business logic

Transformations applied during ETL allow teams to encode business rules upstream. This helps ensure that all analytics are based on a shared, agreed-upon understanding of key metrics and definitions.

Stronger governance and compliance

Because transformations happen before loading, ETL gives teams more control over how sensitive or regulated data is processed. This makes it easier to meet industry requirements and internal data governance policies—particularly in fields like finance and healthcare.

Performance optimization

Transforming data before it reaches the warehouse reduces the compute load during query time. This can improve performance for BI tools and reduce overall warehouse costs.

Centralized, unified data

ETL pipelines bring together data from disparate systems into a single source of truth. This centralized view simplifies reporting, improves data discoverability, and supports more accurate analytics across the business.

The challenges of ETL

While ETL provides strong data governance and reliability, it can present several hurdles—especially in modern, fast-moving environments:

Complex setup and maintenance

ETL pipelines often require significant engineering effort to configure and maintain. As data sources multiply, ensuring smooth, accurate transformations becomes more time-consuming and error-prone.

Time-intensive processes

Transforming data before loading can delay availability—especially for large datasets. This lag can be a challenge for teams that need near-real-time insights.

Limited scalability

Traditional ETL pipelines can become bottlenecks as data volumes grow. Scaling them often requires reengineering or additional infrastructure, which adds cost and complexity.

Higher infrastructure costs

On-premises ETL systems demand dedicated hardware and ongoing maintenance. Even in cloud environments, pre-load transformation can drive up compute costs.

Inflexible transformation logic

ETL workflows require transformations to be finalized before data is loaded. If business needs change, reprocessing data with updated logic can be difficult or impractical.

Business logic buried in BI tools

Some teams defer complex transformations to their BI layer, which can slow performance and obscure logic. Without version control or documentation, it’s hard to trace how key metrics are defined or maintained.

Analyst exclusion from the workflow

ETL pipelines are typically built in engineering-focused languages or tools. This can sideline analysts who understand the data and business context but lack access or expertise in the tooling. As a result, analysts must rely on others to create and update the datasets they need.

Best practices for ETL

To address the complexity and scalability challenges of ETL, teams should adopt practices that promote reliability, maintainability, and trust in their data workflows:

• Use version control for ETL code. Store all ETL pipeline code in a centralized, version-controlled repository. This improves collaboration, enables change tracking and rollback, and helps maintain a consistent development process across teams.
• Build a culture of quality. Treat data workflows like software. Write tests for every new pipeline or change, and enforce code reviews through version control. High-quality, tested ETL pipelines reduce downstream data issues—and their business impact.
• Separate development and production environments. Run ETL development in isolated dev and staging environments before promoting changes to production. This protects live data from accidental errors and allows for safer testing and validation.
• Create and follow a style guide. Establish clear conventions for writing ETL code, naming patterns, documentation standards, and formatting rules. Consistent, readable code helps new contributors ramp faster and reduces long-term maintenance costs.
• Monitor ETL pipelines in production. Track the performance and reliability of ETL jobs after deployment. Set up alerts, monitor for failures, and log performance metrics to catch issues early and keep pipelines running smoothly.

The future of data transformation with ELT and dbt

As data grows in volume and complexity, ELT has become the preferred pattern for modern data transformation—especially in cloud-native environments. By loading raw data into the warehouse first, teams can leverage its computing power to transform at scale, support more use cases, and move faster.

dbt makes this workflow reliable and repeatable. With built-in tools for version control, testing, documentation, and deployment, dbt helps teams manage transformations as code—so your data is always trustworthy, governed, and analysis-ready.

Want to see what dbt can do? Start for free and explore how it fits into your modern data stack.

FAQs about ETL (Extract, Transform, Load)