ETL vs. ELT: What’s the Difference?

Integrating and analyzing data from several sources is important for informed decision-making. Data integration is at the heart of this process of combining data from multiple sources into a single unit.

With the latest estimates putting daily data creation at 402.74 million terabytes, choosing the right data integration approach becomes even more crucial.

Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are the champions of data integration. And they do the same thing; collecting and preparing data for analysis, but in different ways. In ETL, the data is gathered together, transformed, and then loaded into the target system. For ELT, the source data arrives in a target data warehouse such as Amazon Redshift, Snowflake, or SQL Server and then is transformed or enriched at a later stage to meet specific project requirements.

Understanding ETL (Extract, Transform, Load)

ETL (extract, transform, load) is the most traditional data integration method, and it has been in use for decades. Data is extracted from source systems, transformed to fit the functional requirements of the business, and loaded into a consolidated target location, like a data warehouse.

The ETL process starts with the extraction phase, where data is collected from many different sources, such as databases, customer relationship management systems (CRM), or simple flat files.
Next, in the transformation phase, the cleansed data is reorganized to meet any necessary specifications of the target system. By then, it could have been fully migrated, requiring activities such as data normalization, deduplication, or currency conversion.
And finally, data in the transformed state is uploaded into the target system for analysis and reporting.

Key characteristics of ETL include:

Data cleansing and validation before loading
Ability to handle complex transformations
Reduced storage requirements in the target system
Typically batch-oriented processing

ETL is particularly well-suited for scenarios where:

Data requires significant transformation before use
Source systems have limited processing capabilities
Compliance or data governance mandates specific transformations
Historical data needs to be maintained in a specific format

ETL is a powerful, established, and reliable way to integrate data, particularly where transformation requirements are complex or data quality is of a high standard. Meanwhile, as data ecosystems have developed, new forms of ETL have emerged. For example, data integration is now possible using a reverse ETL process.

It means extracting data out of an enterprise data warehouse, transforming it, and then loading it back into operational systems. This helps a company ‘operationalize’ its analytics, adding valuable insights into day-to-day operations.

Understanding ELT (Extract, Load, Transform)

As cloud-based data warehouses became more prominent, their new way of working was formalized under the name ELT, for ‘extract, load, transform’. ELT does exactly what the name suggests: the transformation of data happens after loading it into the target system.

ELT’s capacity to handle large payloads and perform complex transformations on demand makes it a great fit for handling big data and agile enterprise analytics.

The ELT process is similar to ETL in that the data is pulled from its source, but instead of performing the transformation (T) step, the data gets directly pushed into the target data warehouse (or even a data lake), raw and unstructured. Only after it is stored does the transformation step kick in, leveraging the processing power for data manipulation so that it can be shaped for its intended use.

Key characteristics of ELT include:

Faster initial data loading
Ability to store raw data for future use cases
Greater flexibility in transformation logic
Scalability to handle large volumes of data

ELT is particularly effective in scenarios where:

Large volumes of data need to be processed quickly
The target system has significant computing power
Data transformation requirements may change over time
Raw data retention is necessary for compliance or analysis

The ELT approach is also well-suited to the concept of data activation. Data activation is the process of making data useful in a business context by leveraging it in business processes and tools. ELT delivers this by providing fast ingestion of raw data and flexible transformation, enabling businesses to rapidly activate their data for multiple phases and use cases.

Taylor Brown, CEO of ELT company Fivetran, explains how the shift to cloud computing catalyzed the need for ELT when he explains, “That technology that was much more elastic and cloud-based, [and it] was like 10 to 100 times better than the existing data warehouses that were on-premise. This fundamentally changed the way in which customers, companies were setting up their data warehouses; they were replicating everything which allowed for new types of automation — i.e., Fivetran — versus only replicating some pieces and actually transforming data prior to loading it into the database.”

“[ETL] is fundamentally about, hey, these data warehouses are so much faster and so much cheaper, we can refactor the order of operations in a way that is going to be a lot more efficient for humans,” he concludes.

Comparing the key differences between ETL and ELT

When comparing ETL and ELT approaches, there are certain factors to consider. The table below details important differences between these two methods of data integration:

Differentiating Factors	ETL (Extract, Transform, Load)	ELT (Extract, Load, Transform)
Data Volume Handling	Suitable for small to medium data volumes. May struggle with very large datasets due to transformation before loading.	Excels at handling large data volumes. Can easily ingest massive amounts of raw data.
Processing Speed and Efficiency	Generally slower for initial data loading due to upfront transformations. Faster for querying transformed data.	Faster initial data loading. Transformation speed depends on the power of the target system.
Scalability	Limited by the processing power of the ETL server or tool. Scaling up may require additional hardware or resources.	Highly scalable, leveraging the processing power of modern data warehouses. Can easily scale to accommodate growing data volumes.
Flexibility and Agility	Less flexible. Changes in transformation logic often require modifications to the ETL process.	More flexible. Raw data is available for new transformations as business needs change. Allows for agile analytics.
Cost Considerations	May require significant upfront investment in ETL tools and infrastructure. Ongoing costs for maintaining ETL servers.	Often more cost-effective, especially with cloud-based solutions. Pay-as-you-go models available for processing power.
Data Quality Control	Strong data quality control. Errors are caught and addressed before data is loaded.	Data quality checks occur after loading. May require additional steps to ensure data integrity.
Storage Requirements	Requires less storage in the target system as only transformed data is stored.	Requires more storage as both raw and transformed data are typically kept.
Compliance and Auditing	Easier to maintain a clear audit trail of data transformations.	May require additional processes to track transformation history for compliance purposes.
Real-time Processing	Generally batch-oriented. Real-time processing can be challenging.	Better suited for real-time or near-real-time data processing scenarios.
Skill Set Required	Requires specialized ETL developers and tools.	Leverages SQL skills, which are more common in data teams.

In this comparison, it's clear that although ETL is good for enforcing strong data quality control and efficient querying of transformed data, ELT provides more flexibility, scalability, and, usually, cost-effectiveness.

Recognizing these distinctions can help organizations choose the right data integration method for their business strategy and technical capabilities.

The pros and cons of ETL vs ELT

Let’s break down some of those differences into pros and cons, and figure out where each approach best serves a purpose.

ETL

(Extract, Transform, Load)

ELT

(Extract, Load, Transform)

Pros

Better data quality control

Efficient for predefined reporting

Reduced storage in the target system

Suitable for complex transformations

Clear audit trail for compliance

Faster initial data loading

Greater flexibility for changing requirements

Scalable for large data volumes

Leverages modern data warehouse capabilities

Enables agile analytics

Cons

Slower initial data loading

Less flexible for changing requirements

Can struggle with very large datasets

May require specialized ETL tools and skills

Scaling can be costly

Less control over data quality before loading

Requires more storage in the target system

May need additional steps for compliance tracking

Transformation speed depends on the target system

Can be overkill for simple, stable data flows

ETL is particularly effective where data quality and consistent transformations are paramount, as is the case in many data warehousing and business intelligence implementation projects. ETL is also a good fit for any context that requires ‘strong typing’ to ensure correctness and observance of rules (such as many highly regulated, hypercompliant environments—think Fannie Mae or health insurance, just to name a couple).

On the downside, ETL tends to lag behind operational systems, and its computational overheads are reasonably high. In addition, it can be inflexible and suffer when the quantities of data involved become prohibitively large.

But where ELT really excels is in situations with very large data volumes flowing in and where agile analytics capabilities are critical. Using modern data warehouses, ELT is more flexible and scalable, though it would likely involve extra steps to ensure quality control and maintain an audit trail.

Factors to consider when choosing between ETL and ELT for data integration

Ultimately, organizations need to decide what suits their context best by considering a set of factors and criteria that assess their specific needs and capabilities:

Business Requirements
- Consider how fast data needs to be accessed: ELT might be preferable for real-time analytics or fast decision-making.
- Evaluate reporting needs. ELT might be more appropriate if the environment is highly dynamic and will require different data sets. On the other hand, if reporting needs can be anticipated and are relatively fixed, ETL might be preferable for predefined, consistent reports.
- Assess regulatory needs. Companies that deal with sensitive data—for instance, those that need TCPA compliance (Telephone Consumer Protection Act)—would likely benefit from ETL’s upfront data transformation, which would give them greater control and provide a better audit trail.
Data Complexity
- Characterize the variety and structure of data sources. ETL is built to perform complex transformations from diverse sources.
- Consider the volume and velocity of the data. High-volume data or high-velocity streams push you toward selecting ELT.
- Evaluate the requirement for data historization. ELT stores raw data easily and helps maintain historical records.
Existing Infrastructure
- Assess current systems. Organizations with legacy systems—PBX phone systems, for example—may find ETL to be a better fit.
- Consider cloud adoption. ELT often aligns well with cloud-based data warehouses and lakes.
- Evaluate what type of integration will work better with the marketing data platforms that you already have (some may be better tailored to either ETL or ELT processes).
Skill Set of the Team
- Assess the technical skill sets available. ETL demands specialized skills, while ELT utilizes much more common SQL know-how.
- Think about the learning curve. It might be very different to transition to ELT (as opposed to running these processes using more traditional ETL), as teams might need retraining.
- Assess the team's adaptability to new tools and processes.
Budget Constraints
- Compare upfront costs. ETL may require significant initial investment in tools and infrastructure.
- Evaluate ongoing operational costs. ELT can be more cost-effective, especially with cloud-based solutions.
- Consider scalability costs. ELT offers more flexible scaling options, potentially reducing long-term expenses.

With these issues in mind, organizations should choose the most workable option that keeps long-term data strategy goals in mind.

Preparing to implement ETL or ELT: best practices

Whether you choose ETL or ETL for your enterprise data architecture, to make sure that the implementation proceeds without a hitch, here are the main considerations:

Planning and Strategy:
- Define clear objectives and success metrics for the data integration project.
- Map out data sources, transformations, and target systems in detail.
- Create a phased implementation plan, starting with critical data flows.
- Involve stakeholders from IT, data teams, and business units in planning.
Tool Selection:
- Decide which tools will best suit the organization based on its needs, technical environment, and chosen approach (ETL or ELT).
- Consider scalability, ease of use, and integration capabilities with existing systems.
- Assess vendor support, community resources, and the long-term viability of the tools.
- Test potential tools with sample data pipelines before making a final decision.
Data Governance Considerations:
- Establish clear data ownership and stewardship roles.
- Implement data quality checks and validation processes.
- Ensure compliance with relevant regulations and internal policies.
- Develop a metadata management strategy to maintain data lineage and definitions.
- Create documentation for data integration processes and transformations.
Performance Optimization:
- Design efficient data models and transformation logic.
- Implement incremental loading where possible to reduce processing time.
- Use partitioning and indexing strategies in the target system for faster querying.
- Monitor performance regularly and optimize bottlenecks.
- Consider parallel processing for large-scale data operations.
- Implement error handling and logging for easy troubleshooting.

Applying these best practices can help organizations more seamlessly implement their chosen data integration approach, whether ETL or ELT. When their data-related needs change, they can identify opportunities for ongoing review and optimization of the processes.

Making the right choice

On the surface, using ELT and taking advantage of all the benefits we’ve discussed so far might seem obvious. But it is not usually that easy. There are many reasons why ETL has been the norm in data processing, even though ELT is gaining popularity.

In the end, both methods enhance data integrity and quality. ETL's main advantage is its ability to organize data for deeper analysis and inspection around carefully-scoped requirements. In contrast, ELT's main advantage is its speed and flexibility for various use cases.

Selecting the best approach for your organization's data management needs and procedures requires weighing the benefits and drawbacks of both methods. Either will get rid of annoying data silos and increase visibility throughout the organization. Still, ultimately, the right choice will depend on your business requirements, data goals, and the existing data management infrastructure.

Curious to learn how a data intelligence platform can support your ETL or ELT strategy? Book a demo with us to learn more.