Understanding ETL: Extract, Transform, Load

Effective data management has become a cornerstone for organizations aiming to turn information into insights. As businesses gather data from multiple systems, organizing it into a structured format becomes critical for analytics and decision-making.

ETL, which stands for Extract, Transform, Load, is a key data integration process that businesses use to organize, clean, and prepare their data for analysis. The Extract, Transform, Load (ETL) process enables companies to consolidate raw data, clean and transform it, and store it in a data warehouse for analysis. However, with the rapid shift to cloud computing and the increasing volume of unstructured data, a newer approach—ELT (Extract, Load, Transform)—has gained traction.

In this article, we’ll explore the ETL process, its benefits, challenges, and use cases across industries. We’ll also take a look at how the modern data stack is evolving to accommodate ELT.

What is ETL?

ETL is the process of ingesting data from various sources (transactional database, customer support tool, advertising platforms, etc.) into a centralized data warehouse, transforming or normalizing it for upload to the warehouse, and finally loading it into the warehouse.

ETL has been the go-to approach for data warehousing and management for decades, enabling businesses to gather, clean, and analyze data in a structured manner.

This process helps organizations consolidate data from multiple systems, apply business logic, and ensure data quality before it reaches the final destination for analysis. It’s essential for ensuring that business intelligence tools can accurately query and report on reliable, clean data.

Who performs ETL?

Analytics engineer or data engineer, depending on the complexity of configuration and your team structure. If data is coming from commonly-used APIs (ex: Shopify, Stripe), then an analytics engineer can use off-the-shelf data loaders (ex: Fivetran, HVR, Stitch) to configure their own integrations.

A data engineer may use no-code data integration tools as well, because Engineers Shouldn’t Write ETL. If the integrations required are not commonly supported, then a data engineer would step in to script and deploy a custom tap.

Generally, writing data extraction scripts falls outside the job description of an analytics engineer, although many AEs technically could write API integrations.

The history and evolution of ETL

ETL has a long history in data management, evolving alongside advancements in technology:

Early days of ETL

Extract, Transform, Load processes trace back to the 1970s and 1980s when businesses started using large-scale data systems for business intelligence (BI) and reporting. Early ETL tools were batch-oriented, meaning data would be extracted, transformed, and loaded during scheduled times—typically during off-peak hours.

Data was extracted from on-premises relational databases, transformed to ensure consistency and quality, and loaded into data warehouses designed to support analytical reporting.

Rise of data warehouses in the 1990s

As the need for more sophisticated reporting grew in the 1990s, data warehouses became central to business intelligence efforts. During this time, data transformation processes matured, with companies like Informatica and Talend offering tools to automate the process.

The ability to extract data from various systems, cleanse and format it, and then load it into centralized warehouses became critical for decision-making.

Cloud and big data in the 2000s

With the rise of cloud computing and big data platforms in the early 2000s, ETL processes evolved to handle larger datasets and more complex data transformations. Cloud-based data warehouses like Amazon Redshift, Snowflake, and Google BigQuery offered scalable infrastructure, allowing businesses to load and process massive amounts of data efficiently.

The shift to ELT

Today, as more businesses adopt cloud-native architectures, the traditional ETL process is evolving into ELT. With ELT, the raw data is loaded into the data warehouse first, and transformations are performed inside the warehouse, taking advantage of its scalable computing power. This shift is particularly valuable for organizations working with large, unstructured datasets that require real-time analysis.

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 adoption is growing, we still see ETL use cases for processing large volumes of data and adhering to strong data governance principles.

ETL to efficiently normalize large volumes of data

ETL can be an efficient way to perform simple normalizations across large data sets. Doing these lighter transformations across a large volume of data during loading can help get the data formatted properly and quickly for downstream use. In addition, end business users sometimes need quick access to raw or somewhat normalized data. Through an ETL workflow, data teams can conduct lightweight transformations on data sources and quickly expose them in their target data warehouse and downstream BI tool.

ETL for hashing PII prior to load

Some companies will want to mask, hash, or remove PII values before it enters their data warehouse. In an ETL workflow, teams can transform PII to hashed values or remove them completely during the loading process. This limits where PII is available or accessible in an organization’s data warehouse.

Benefits of ETL

Extract, Transform, Load offers several benefits, especially for businesses looking to maintain high standards of data quality, governance, and performance. Benefits include the following:

Data quality and consistency

ETL ensures that data is cleaned, standardized, and validated before it enters the data warehouse. By applying transformation logic early in the process, businesses can trust that the data available for analysis is accurate and consistent across all sources.

Business logic application

The transformation phase of ETL allows organizations to apply specific business rules and logic to the data. This ensures that the data conforms to the organization’s unique requirements before it reaches the warehouse.

Data governance and compliance

ETL offers a structured process for ensuring data governance and compliance. In industries like finance and healthcare, where regulatory compliance is critical, ETL ensures that data transformations adhere to strict rules before it is loaded into a system.

Performance optimization

By transforming data before it is loaded, ETL reduces the amount of processing required within the data warehouse, improving query performance. This ensures faster retrieval times for analytics and reporting, particularly when working with large datasets.

Centralized data

ETL enables businesses to consolidate data from various sources into a single, centralized data warehouse. This unified view of data makes it easier for analysts to access, query, and report on data from multiple systems.

The challenges of ETL

While Extract, Transform, Load offers many advantages, it’s not without its challenges. Businesses should be aware of these limitations when designing their ETL workflows.

Complexity of setup and maintenance

Building and maintaining an ETL pipeline can be complex, especially as data sources grow in number and variety. It requires significant time and effort from data engineers to ensure the pipeline is functioning smoothly and handling transformations correctly.

Time-consuming processes

ETL, particularly the transformation phase, can be time-intensive. For large datasets, the process of cleaning and transforming data before loading can introduce delays in data availability.

Scalability issues

Traditional ETL processes can struggle to scale efficiently as data volumes grow. Organizations dealing with large, rapidly expanding datasets may find that their ETL pipelines become bottlenecks, slowing down data availability.

Cost implications

Implementing and maintaining an ETL infrastructure, especially in on-premise environments, can be expensive. As data volumes increase, the need for more processing power and storage can drive up costs.

Rigidity in transformation logic

Because ETL transforms data before loading it into the warehouse, making changes to transformation logic after the data has been loaded can be difficult. This rigidity limits the ability to adapt the transformation logic to new business requirements without reprocessing the data.

Immense amount of business logic living in BI tools

Some teams with ETL workflows only implement much of their business logic in their BI platform versus earlier in their transformation phase. While most organizations have some business logic in their BI tools, an excess of this logic downstream can make rendering data in the BI tool incredibly slow and potentially hard to track if the code in the BI tool is not version controlled or exposed in documentation.

Data analysts can be excluded from ETL work

Because ETL workflows often involve incredibly technical processes, they've restricted data analysts from being involved in the data workflow process. One of the greatest strengths of data analysts is their knowledge of the data and SQL, and when extractions and transformations involve unfamiliar code or applications, they and their expertise can be left out of the process. Data analysts and scientists also become dependent on other people to create the schemas, tables, and datasets they need for their work.

Use cases and examples of Extract, Transform, Load in action

ETL is used across industries to solve a wide range of data integration and transformation challenges. Here are a few industry-specific use cases where ETL plays a vital role.

Retail

Retailers collect data from various sources, such as e-commerce platforms, in-store point-of-sale systems, inventory management software, and customer loyalty programs. ETL processes help consolidate all of this data into a single data warehouse. Retailers can then analyze this data to track sales trends, manage inventory levels, and better understand customer behavior.

Finance

Financial institutions rely heavily on ETL to ensure accurate reporting, risk management, and regulatory compliance. ETL processes extract transaction data from banking systems, apply business rules to filter out fraudulent activity, and load the data into a warehouse for reporting. This consolidated view allows for more effective auditing and monitoring of financial activity.

Healthcare

In healthcare, ETL is used to integrate patient data from different hospital departments, electronic medical records (EMR) systems, and billing platforms. The ETL process ensures that patient data is accurate and up-to-date before being loaded into a centralized system, where it can be used for analysis, reporting, and improving patient care.

Marketing and advertising

Marketing teams use ETL to integrate data from multiple advertising platforms, email marketing systems, and CRM systems. By extracting campaign performance data, transforming it into standardized metrics, and loading it into a centralized dashboard, marketing teams can optimize their ad spend and better understand their return on investment (ROI).

Common questions about ETL

What does ETL stand for?

ETL stands for Extract, Transform, Load, a process used to collect, clean, and load data into a data warehouse for analysis.

What’s the difference between ETL and ELT?

In ETL, data is transformed before being loaded into the warehouse. In ELT, raw data is loaded first, and transformations are applied inside the warehouse.

Why is the Extract, Transform, Load process important?

ETL ensures that data is clean, structured, and ready for analysis, providing a reliable foundation for business intelligence and decision-making.

What are some common ETL tools?

Popular ETL tools include Informatica, Talend, Fivetran, and Apache NiFi.

ETL vs ELT

As the volume and complexity of data have grown, traditional ETL processes have encountered limitations in terms of scalability and flexibility. Enter ELT (Extract, Load, Transform), a modern approach that takes advantage of the computational power of cloud-based data warehouses.

What is ELT?

ETL tools extract, transform and load data from APIs and external sources into a data warehouse. ELT flips the traditional ETL sequence by loading raw data into the data warehouse first, and then applying transformations within the warehouse itself. 

With this final transformation step at the end of the ELT workflow, we have the power to work like software engineers.

This approach leverages the scalable, high-performance computing resources of modern cloud environments, making it ideal for handling massive datasets that may be difficult to process in a traditional ETL pipeline.

When to use ETL vs ELT

ETL remains the preferred choice for industries or scenarios that require strict data governance, compliance, and highly curated datasets. For example, financial institutions and healthcare organizations often rely on ETL to ensure that sensitive data is transformed, cleansed, and validated before it reaches the data warehouse.

ELT is more commonly used in cloud-native environments, where businesses can take advantage of the data warehouse’s processing power. ELT is ideal for organizations that need flexibility and scalability, as it allows them to load raw data quickly and apply transformations later as needed.

ETL and ELT hybrid models

While ETL and ELT are often discussed as separate approaches, many organizations use a hybrid model that combines the strengths of both.

In many cases, businesses adopt a hybrid approach. For example, they might use ETL for critical, sensitive data that requires transformation before loading, but use ELT for less structured, high-volume data that can be transformed after it is in the warehouse.

This hybrid model offers the best of both worlds—maintaining data governance and control while leveraging cloud-based scalability for certain datasets.

The future of data transformation with ELT and dbt Cloud

As data continues to grow in volume and complexity, ELT has emerged as the new standard for data transformation in the cloud. By loading raw data into modern data warehouses first and transforming it later, businesses can benefit from faster, more scalable data pipelines.

dbt Cloud enhances the ELT process by providing robust tools for managing, automating, and testing data transformations directly in the warehouse. This ensures that data is always clean, reliable, and ready for analysis.

Sign up for a free dbt Cloud account today to start optimizing your data transformation process and ensure that your data pipelines are scalable, efficient, and future-proof.