Understanding ELT: Extract, Load, Transform

As businesses continue to generate more data than ever before, the methods used to store, process, and analyze that data have evolved significantly.

Traditionally, the ETL (Extract, Transform, Load) process was the standard for moving data from multiple sources into a centralized system. However, with the rise of cloud-based infrastructures and the increasing complexity of data, ELT (Extract, Load, Transform) has emerged as a more flexible and scalable alternative.

What is ELT?

ELT, which stands for Extract, Load, Transform, is a data integration process that flips the traditional ETL sequence. In an ELT workflow, we separate the data extraction and transformation steps—source data would make its way into the warehouse (“EL”), and then we could transform it to our heart’s content (the “T”).
The transformation step occurring within the warehouse, leverages the data warehouse's computational power.

This would give our team of analytics & data engineers much more flexibility when building data transformation logic. We’d always have the raw source data available, and could iterate on how we transformed it after the fact.

This approach is well-suited for cloud-based data warehouses like Snowflake, BigQuery, and Amazon Redshift, which are designed to handle large datasets efficiently and can process complex transformations at scale.

ELT vs ETL

In ETL, data is transformed before being loaded into the data warehouse, whereas in ELT, raw data is loaded first and transformations are applied later. ELT is better suited for cloud environments where scalability and performance are critical.

When to use ELT vs ETL

ELT is best suited for organizations that need flexibility, scalability, and fast access to raw data. Cloud-native businesses often benefit from ELT’s ability to process large datasets and apply transformations iteratively.

ETL is ideal for industries that require strict data governance, such as finance and healthcare. In these cases, data needs to be transformed, cleaned, and validated before entering the warehouse.

Why use ELT?

If you’ve worked in an ELT workflow, you know inherently why it improves your life as a data person. Being a skilled data archaeologist requires being able to quickly and efficiently gain understanding by performing key investigative tasks:

1. Recreate historical transformations: Can we easily rebuild this modeled data from source data?
2. Trace data model dependencies: Can we inspect what lower-level data models (stored procedures in our ETL case) rolled up to a given fact or dimension model?
3. QA test analytics code changes: Can we ensure that changes being pushed to transformation code won’t introduce data quality issues?

Let’s dig into each of these tasks, and why an ELT workflow makes them significantly easier than ETL.

Recreate historical transformations

Transformation code changes over time, and since records are transformed incrementally, the meaning of records would be time-dependent. A field would be defined one way, and later on, a different way—it literally required archaeological layering of definitions.

With an ELT workflow, we could implement proper change management for transformation code: when we deploy an update to a field definition, the change can apply across all historical data, rather than only newly-created records.

This means that data transformations would be idempotent—if we recreate the data warehouse with the click of a button or the run of a command, and source data or transformation logic doesn’t change, we get the same results.

Trace data model dependencies

As a developer, it took a ton of investigation to uncover how a given record came to be. We had to trace that flow point-by-point through multiple stored procedures for the record to see how it got where it ended up. We were not able to easily recreate how the record got to its final state—there was no centralized, documented dependency graph for us to trace directly. We had to do it live, by hand.

Now, this isn’t strictly a problem with ETL vs ELT: this is a problem with using a series of layered stored procedures to perform data transformations.

When migrating to ELT, we could implement a tool like dbt as the transformation layer, which would infer our data transformation DAG and make it searchable via automatically-generated documentation.

QA test analytics code changes

We really do not want to introduce any errors, since we’d have such a hard time tracking them down—but we didn’t have any sandbox to easily test changes to the stored procedure pipeline.

But even then, I didn’t feel confident about pushing changes, because I didn’t have an automated way to run those tests, and it wasn’t in my wheelhouse to spin up the environment to run them locally. There were just so many unknowns, and it was very time consuming to iron out differences that popped up during testing.

The ELT workflow on its own, of course, wouldn’t completely prevent us from introducing issues in data as a result of changes—but dbt certainly would give our developers the tools (version control, easy creation of dev environments, data + schema tests) to be much more confident that we weren’t breaking things.

The history and evolution of ELT

While ETL has been around for decades as the primary method for processing data, the evolution of cloud technologies has led to the rise of ELT as a modern alternative.

Early days of ETL

The Extract, Transform, Load process originated in the 1970s and 1980s, when data warehouses were first introduced. During this period, data extraction, transformation, and loading were necessary steps to organize data for business intelligence (BI) and reporting. Data was typically extracted from various sources, transformed to meet business requirements, and then loaded into a centralized warehouse for analysis.

ETL became especially important in industries that needed to process structured data efficiently. However, as the volume of data increased, traditional on-premise ETL systems struggled to keep pace.

The shift to ELT

The 2000s saw the rise of cloud computing, which provided organizations with scalable, on-demand storage and processing power. This shift created an opportunity for the Extract, Load, Transform process, where data could be loaded into cloud data warehouses first and transformed later.

By loading raw data into the warehouse, organizations could perform transformations at scale using the cloud's vast computational resources. This new process significantly reduced the time to insight, as data became available for analysis much faster than in the traditional ETL model.

What is the Extract, Load, Transform process?

The Extract, Load, Transform (ELT) process consists of three stages: Extract, Load, Transform.

Extract:

• Raw data is extracted from various sources such as relational databases, CRM systems, cloud applications, or APIs. The data is then stored in a temporary staging area or sent directly to the data warehouse.

Load:

• Unlike ETL, where data is transformed before loading, ELT loads raw data into the data warehouse immediately after extraction. This step is efficient because it doesn't require any transformation before the data enters the warehouse, allowing for faster data availability.

Transform:

• Once the raw data is loaded into the data warehouse, the transformation process begins. Data engineers and analysts apply business logic, cleanse the data, and transform it into a structured, usable format. The transformations are performed within the data warehouse using its computational resources. This phase can include data filtering, aggregation, and applying business rules.

The benefits of ELT

ELT offers several benefits, particularly in cloud-based environments where scalability, flexibility, and performance are essential. Key benefits include:

Scalability

ELT leverages the massive computational power of modern cloud data warehouses, allowing organizations to scale their data pipelines effortlessly. As data volumes grow, ELT can handle the increased workload without performance bottlenecks.

Faster data availability

By loading raw data into the warehouse immediately after extraction, ELT makes data available for analysis much faster than ETL. This is especially beneficial for organizations requiring near-real-time insights from their data.

Cost efficiency

ELT reduces the need for expensive on-premise ETL tools and infrastructure. With ELT, businesses can offload processing tasks to the cloud and only pay for the resources they use. This makes it a cost-effective solution for companies managing large datasets.

Flexibility

ELT allows for more flexibility in data transformation. Since raw data is stored in the warehouse, analysts can apply transformations iteratively, making it easier to adapt to changing business requirements.

Simplified pipelines

The ELT process simplifies the data pipeline by removing the need to transform data before loading it into the warehouse. This reduces the complexity of data integration and improves overall pipeline management.

The challenges of ELT

Despite its advantages, ELT has its own set of challenges that organizations must consider when implementing this approach.

Increased storage needs

ELT requires storing raw, untransformed data in the data warehouse, which can lead to increased storage costs, especially for organizations handling large volumes of unstructured data.

Complexity in transformation management

While ELT allows for more flexibility in transforming data within the warehouse, managing transformations at scale can be complex. Organizations need to ensure that data quality is maintained throughout the process, and transformations are applied correctly.

Potential performance bottlenecks

Although ELT takes advantage of cloud computing power, large and complex transformations can still lead to performance bottlenecks if not optimized properly. Monitoring and tuning the transformations within the data warehouse are essential to maintain performance.

Security and compliance concerns

Storing raw, untransformed data in the warehouse may pose security and compliance risks, especially for industries that handle sensitive data. Organizations must implement strict data governance policies to ensure compliance with regulations like GDPR or HIPAA.

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

ELT is used across a wide range of industries that require scalable, cloud-based data processing solutions. Here are some examples of how ELT is used in practice.

E-commerce

E-commerce companies use ELT to process and analyze data from online sales platforms, customer interactions, and inventory management systems. By loading raw data into a cloud data warehouse, companies can apply various transformations to track customer behavior, optimize inventory, and forecast demand.

Healthcare

ELT allows healthcare providers to collect and store patient data from multiple sources such as EMR systems, lab results, and billing platforms. Transformations are applied after the data is loaded to ensure data accuracy and compliance with healthcare regulations.

Finance

Financial institutions rely on ELT to handle high-volume transaction data. By loading the data into a cloud data warehouse, banks and financial services companies can apply business rules and transformations to detect fraudulent activity and ensure regulatory compliance.

Marketing and advertising

Marketing teams use ELT to integrate data from multiple advertising platforms, email marketing systems, and social media channels. After loading raw data into the warehouse, analysts apply transformations to measure campaign performance, analyze customer engagement, and optimize ad spend.

Common questions about ELT

What does ELT stand for?

ELT stands for Extract, Load, Transform, a process in which raw data is extracted, loaded into a data warehouse, and then transformed within the warehouse.

How is ELT different from ETL?

In ETL, data is transformed before being loaded into the data warehouse, whereas in ELT, raw data is loaded first and transformations are applied later. ELT is better suited for cloud environments where scalability and performance are critical.

Why is ELT important?

ELT is important because it allows organizations to process large datasets more efficiently by leveraging the cloud’s computational power. It also provides faster access to raw data, which can be transformed as needed.

What are the benefits of ELT over ETL?

ELT offers faster data availability, greater scalability, and lower infrastructure costs compared to ETL. It also provides more flexibility in applying transformations within the data warehouse.

When to use ETL (Extract, Transform, Load)

ETL is ideal for industries that require strict data governance, such as finance and healthcare. In these cases, data needs to be transformed, cleaned, and validated before entering the warehouse.

When to use ELT (Extract, Load, Transform)

ELT is best suited for organizations that need flexibility, scalability, and fast access to raw data. Cloud-native businesses often benefit from ELT’s ability to process large datasets and apply transformations iteratively.

Hybrid approach

A hybrid model combines both ETL and ELT, allowing businesses to transform critical data before loading and apply transformations to larger datasets post-loading. This provides the best of both worlds by ensuring data governance and scalability.

The future of data transformation with ELT and dbt Cloud

As more organizations shift toward cloud-based infrastructures, ELT will continue to play a crucial role in data transformation processes. With its ability to handle large volumes of data and its flexibility in transforming data after it’s loaded, ELT is ideal for modern, data-driven businesses.

dbt Cloud enhances the ELT process by offering a comprehensive transformation layer within the warehouse. By automating, version-controlling, and testing data transformations, dbt Cloud ensures that businesses can rely on their data for accurate insights.

Sign up for a free dbt Cloud account today to start managing your ELT transformations more effectively and ensure your data pipelines are scalable, secure, and future-ready.