ETL vs ELT: Understanding the Key Differences, Pros & Cons, and Use Cases

The global big data analytics market will grow from USD 348.21 billion in 2024 to USD 924.39 billion by 2032. With this increase, ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) will be key in processing these growing data. But the differences between ETL vs ELT can be confusing.

ETL has been used for decades and is a mature approach. It gave many businesses value from meaningful insights. Meanwhile, ELT was born from the cloud revolution, enabling enterprises to handle data at scale.

Choosing between the two approaches is a strategic decision. This article will break down ETL vs ELT, and compare their workflow, performance, use cases, and pros and cons.

Let's begin.

What are ETL and ELT?

The acronyms elude many. Some even misspell them. This section will provide the definition, how it works, and other brief details for each.

What is ETL (Extract, Transform, Load)?

ETL stands for Extract, Transform, Load. It's a traditional data integration process used to consolidate data from multiple sources into a central repository (like a data warehouse) or another system.

For example, a company wants to analyze its CRM, E-Commerce website, and Helpdesk data. To open the way for deeper analysis, data from these sources are consolidated into a staging area and then into a data warehouse. This is illustrated below:

ETL emerged in the 1970s for on-premises data warehouses where storage and processing power were limited. It then grew to include integrating data between systems. Today, ETL can run either on-premises, in the cloud, or hybrid.

How It Works

The sequence of letters in the acronym is the flow of the process:

• Extract (E): Data is pulled from various sources (databases, APIs, files).

• Transform (T): Data is cleaned, formatted, and enriched before storage.

• Load (L): Transformed data is loaded into a target database or warehouse.

Below is a diagram of how ETL works:

Popular ETL Tools

There are many tools that support ETL processes. The following are some of them:

• Informatica: Enterprise-grade data integration platform.

• Talend: Open-source ETL tool for big data and cloud integration.

• Pentaho: Business intelligence and data integration suite.

• Skyvia: A cloud-based data platform supporting ETL with import integration and a flexible control flow and data flow.

What is ELT (Extract, Load, Transform)?

ELT stands for Extract, Load, Transform. It's a modern approach where data is first loaded into a data lake or cloud warehouse, and transformations happen afterward using powerful cloud processing engines.

ELT gained popularity with cloud-native services that offer scalable storage and processing, like Snowflake, BigQuery, and Azure Synapse. It is also flexible and faster to develop and adjust than ETL pipelines.

How It Works

Similar to ETL, the acronym reveals the process flow:

• Extract (E): Data is pulled from source systems.

• Load (L): Raw data is loaded directly into the target system. Note that raw data does not mean loading everything blindly. Sensitive data are either masked, encrypted, or not included.

• Transform (T): Data is transformed using the target system's computing resources.

Below is a diagram of how ELT works:

Popular ELT Tools

The following are tools commonly used for ELT processing:

• dbt (Data Build Tool): An open-source Transformation engine within cloud warehouses. It only does the transformation part of the process.

• Matillion: Cloud-native ETL/ELT platform.

• Skyvia: Also supports ELT with replication aside from ETL.

ETL vs ELT: Key Differences

Differences are seen in architecture, transformation step, performance, scalability, security, compliance, and cost. Our consideration of ETL is divided into two categories: on-premises and cloud. Both have their place in data integration scenarios today along with ELT.

1. Architecture

Architecture involves the working environments of data integration scenarios. This includes hardware, software, platforms, and tools used by both ETL and ELT. It affects performance, scalability, and flexibility of the pipeline.

The following are the working environments of each:

ETL (On-Premises):

• Designed for on-premises environments with dedicated ETL servers.

• Data staging area may be needed for transformations depending on requirements.

• Suitable for structured data with well-defined schemas.

ETL (Cloud):

• Operates in cloud-based environments using fully managed services like Skyvia, AWS Glue, or Azure Data Factory.

• No infrastructure management, reducing operational overhead.

• Supports hybrid data integration from multiple cloud services and on-prem systems.

ELT:

• Cloud-native by design, leveraging cloud data warehouses (e.g., Snowflake, BigQuery).

• Direct data ingestion into storage. Transformations are added later.

• Ideal for modern, large-scale data projects.

2. Data Transformation Timing

Data will undergo specific transformations depending on the system or report requirements. It can be pivoting, aggregating, joining, filtering, adding a new column, and more. Without transformation, data may have no relations at all and reports are difficult or sometimes impossible to produce.

When this step happens is the notable differentiator between ETL vs ELT.

ETL (Both On-Premises and Cloud):

• Before loading data into the destination system. This ensures only clean and organized data enters storage.

• On-premises: Requires dedicated resources for transformation.

• Cloud: Offloads transformation to managed services, making scaling easier.

ELT:

• Transformation happens after loading into cloud storage.

• Enables raw data analysis for deeper insights but requires powerful query engines.

3. Performance and Scalability

Performance refers to how fast a data integration runs. This is measured in time units like seconds, minutes, or hours. Scalability, however, is the ability to handle increasing volumes of data without compromising performance.

As users have limited patience to wait for an integration process to finish, performance and scalability are critical factors. Below are the differences in ETL vs ELT in this matter:

ETL (On-Premises):

• Limited scalability due to hardware constraints.

• Performance depends on server capacity and database tuning. Reaching full capacity needs hardware and software upgrades to process growing data volumes.

ETL (Cloud):

• Auto-scaling features ensure near-unlimited scalability.

• Can process data in real-time or batch using cloud infrastructure.

ELT:

• Highly scalable due to cloud-native design.

• The loading process is faster than ETL as transformations are deferred for later.

• Massive parallel processing allows for fast, large-scale transformations.

4. Cost Efficiency

Platform choice, scalability needs, and more have operational costs. Balancing data integration requirements against cost can be challenging. That's why it is another concern for businesses to consider data integration solutions. Companies want it as predictable as possible. It should fit with their capital and operating expenditures.

The following are the cost considerations between ETL vs ELT:

ETL (On-Premises):

• High initial costs for hardware, licensing, and maintenance.

• Predictable costs but can become expensive with scaling needs.

ETL (Cloud):

• Pay-as-you-go model lowers upfront expenses.

• Cost optimization tools from cloud providers help control expenses.

ELT:

• Low initial costs since data warehouses handle transformations.

• Storage and compute costs can rise quickly with raw data storage and complex queries.

5. Security and Compliance

Protecting data and complying to regulations cannot be ignored in today's privacy-minded world. Data leaks and system compromises are increasing every year. The choice between ETL vs ELT is crucial to avoid legal disputes.

ETL (Both On-Premises and Cloud):

• Pre-load data transformations help enforce data privacy and compliance standards.

• Cloud ETL adds layers like encryption, role-based access, and compliance certifications (e.g., GDPR).

ELT:

• Data governance policies must be enforced at the storage and query level.

• Requires advanced security features like encryption, access management, and audit trails.

ETL vs ELT Comparison Table

Let's have a side-by-side comparison between ETL vs ELT. The following table summarizes the basic comparison for easier reference.

ETL vs ELT Pros and Cons

Both ETL and ELT have their strengths and weaknesses. This section will help organizations decide between ETL vs ELT based on their advantages and disadvantages.

ETL Pros and Cons

The following is where ETL excels and where it does not play well. This will cover costs, maturity, security, compliance, and others.

Pros:

• Data Structure Control: ETL works well with structured data, ensuring consistent and clean datasets before loading.

• Data Compliance & Security: On-premises ETL is ideal for highly sensitive data due to complete control over infrastructure. Cloud ETL providers like Skyvia offer enterprise-grade security features.

• Mature Technology: Widely used for decades with a vast tool ecosystem (e.g., Talend, Informatica, SSIS).

• Custom Transformation Logic: This may support custom scripts and transformations using SQL, Python, or Java depending on the tool.

Cons:

• Infrastructure Costs (On-Premises): Requires expensive hardware, maintenance, and IT staff.

• Data Compliance & Security: On-premises ETL is ideal for highly sensitive data due to complete control over infrastructure. Cloud ETL providers like Skyvia offer enterprise-grade security features.

• Limited Real-Time Processing: Works best with batch processing, which can cause latency for time-sensitive data.

• Complex Setup: Cloud ETL simplifies deployment but can still involve complex data mappings and configurations. It may be difficult to adjust when changes to the pipeline are needed.

• Scalability Limits (On-Premises): Scaling requires adding more hardware, which is both costly and time-consuming.

ELT Pros and Cons

Below are the points where ELT works best and where it does not play well. This will also cover costs, security, and other concerns.

Pros:

• Big Data & Real-Time Support: Massive data processing at scale with real-time analytics capabilities.

• Simpler Architecture: No data staging area is needed; raw data is directly loaded into cloud warehouses like Snowflake or BigQuery.

• Automatic Scaling: Cloud ELT scales automatically based on usage and workloads, reducing manual effort.

• Reduced Maintenance: Serverless services handle infrastructure management, freeing up developers' time.

Cons:

• Higher Storage Costs: Storing raw data can get expensive in cloud warehouses.

• Complex Transformations: Advanced transformations can be complex and may require specialized tools like dbt.

• Security Concerns: Cloud data security depends on vendors and sensitive data requires robust encryption and compliance measures.

• Vendor Lock-In: Switching cloud providers can be costly and difficult due to proprietary solutions.

Use Cases for ETL and ELT

This section explores practical industry-specific use cases for ETL and ELT. It will consider different business needs, data types, and operational models. We will also touch on hybrid approaches where both processes can coexist.

ETL Use Cases

ETL is best for structured data, legacy systems, strict data governance, and batch processing. Below are three common use cases, why ETL is the preferred choice, and the tools involved.

1. Financial Services (Batch Processing)

Scenario: A bank processes daily transaction records for compliance and fraud detection.

Why ETL: Data requires complex transformations for reporting. Security regulations mandate on-premises processing before loading data into warehouses and consumed by reports.

Example Tools: Informatica, Talend, SSIS, Skyvia.

2. Healthcare (Structured Patient Records)

Scenario: A healthcare provider integrates patient records from various clinics.

Why ETL: HIPAA compliance requires data masking and strict encryption before storage. Scheduled batch loads ensure up-to-date records for patient management systems.

Example Tools: Oracle Data Integrator, Pentaho.

3. Insurance (Data Warehousing)

Scenario: Insurance firms need to consolidate policy data for annual audits.

Why ETL: Complex business logic needs to be applied. Data standardization is crucial for creating consolidated reports.

Example Tools: Skyvia, SSIS.

ELT Use Cases

ELT is best for real-time analytics, big data, cloud-native platforms, and unstructured data. It is common to see this used in scenarios like IoT sensor data, e-commerce, and entertainment media. The following shows why ELT excels in these areas along with the tools used.

1. E-commerce (Real-Time Analytics)

Scenario: An online store tracks customer behavior for personalized marketing.

Why ELT: Cloud data warehouses like Snowflake enable real-time reporting. Raw customer interactions are ingested and analyzed instantly.

Example Tools: dbt, Google BigQuery, Matillion.

2. IoT (Sensor Data Processing)

Scenario: A smart device company processes real-time IoT sensor data from devices worldwide.

Why ELT: The high volume and velocity of data make on-premises transformation impractical. Cloud processing enables near-instant insights.

Example Tools: Azure Synapse, Amazon Redshift.

3. Media & Entertainment (Content Analytics)

Scenario: Insurance firms need to consolidate policy data for annual audits.

Why ETL: Complex business logic needs to be applied. Data standardization is crucial for creating consolidated reports.

Example Tools: Skyvia, SSIS.

Hybrid Use Cases (ETL + ELT)

Some organizations do not stick to one solution. They use both ETL and ELT depending on data types, regulatory needs, and cloud adoption levels. Computing resources are also balanced when using a solution to a certain scenario. For example, batch processing may be applicable in certain reports, like year-end reports. Using real-time processing may be overkill.

Consider the two scenarios below and why a hybrid solution is best for each.

1. Retail (Inventory + Customer Data)

Scenario: A retailer processes inventory data with ETL and customer behavior analytics with ELT.

Why Hybrid: Inventory data is structured and processed nightly for sales reports. Customer analytics require real-time insights to adjust promotions on the fly.

2. Telecom (Billing and Usage Analytics)

Scenario: A telecom company processes monthly bills with ETL but uses ELT for real-time usage alerts.

Why Hybrid: Billing data needs strict accuracy and compliance. Usage alerts benefit from real-time streaming.

Performance and Scalability of ETL vs ELT

Let's dive deeper into how ETL and ELT perform in their respective environments, considering scalability, data processing models, and cost-efficiency. An organization can benefit from knowing these factors affecting their strategies moving forward.

Data Processing Architecture

ETL pre-processes data using transformation before loading. With on-premises ETL, the performance impact are the following:

• Centralized data processing on dedicated servers can become a bottleneck if data volumes grow too fast.

• Latency Risk: Transforming large datasets locally may cause processing delays.

But cloud ETL is different. Utilizing the cloud, below are the performance impact:

• Flexible Processing: Cloud ETL services (e.g., Skyvia, AWS Glue) use scalable cloud infrastructure.

• Lower Latency: Data can be processed near real-time if cloud-based ETL engines handle the transformation.

Meanwhile, ELT transforms data after loading. This has the following impact on performance:

• Uses cloud-native platforms (e.g., Google BigQuery, Azure Synapse) for massive parallel processing.

• Fast Data Availability: Data is ready for querying almost immediately after loading.

Scalability and Resource Utilization

As data volumes grow over time, ETL and ELT behave differently. Let's cover the scaling model and resource usage of each.

Check out the following table for comparison:

Performance Optimization Strategies

ETL performance optimization strategies for both on-premises and cloud are the following:

• Parallel Processing: Split large jobs into smaller, concurrent tasks.

• Incremental Data Loads: Only process changed data using CDC (Change Data Capture).

• Job Scheduling: Automate jobs to run during off-peak hours.

Meanwhile, ELT optimization can be one or more of the following:

• Data Partitioning and Clustering: Organize data for faster queries in platforms like Snowflake.

• Query Tuning: Optimize SQL queries to minimize processing time. Together with partitioning and indexing, query performance can increase.

• Resource Auto-Scaling: Enable auto-scaling features in services like BigQuery and Azure Synapse.

When to Choose ETL vs ELT

This section shows when to select ETL or ELT based on factors like architecture, use case, and performance needs. Both on-premises and cloud ETL will be considered alongside ELT to present a complete picture.

When to Choose ETL

Consider on-premises ETL when legacy system and sensitive data exists, and data volumes are very predictable. Below are the details:

• Legacy Systems: Ideal for organizations with long-standing data warehouses and no plans to move to the cloud. On-premises ETL is also compatible with systems requiring tight integration with on-premises infrastructure.

• Sensitive Data: Transform sensitive data locally before loading it into storage. This complies with strict data security or regulatory policies.

• Stable Workloads: Works best when data volumes and transformation needs are predictable and manageable with existing resources.

Consider cloud ETL if the following conditions are true to your organization:

• Hybrid Architectures: If data sources are on-premises but processing and storage are cloud-based, use cloud ETL to transform and load efficiently.

• Scalable Transformations: Organizations that need on-demand scalability for transforming large datasets benefit from cloud ETL.

• Budget Constraints: Cloud ETL tools (e.g., Skyvia, AWS Glue) eliminate the need for large upfront hardware investments.

When to Choose ELT

Consider ELT for the following scenarios:

• Cloud-Native Architectures: Ideal for organizations fully operating in the cloud with platforms like Snowflake, BigQuery, or Azure Synapse. Use cloud providers' compute power for transforming data post-load.

• Real-Time Analytics: Faster access to raw data makes ELT the go-to choice for real-time or near-real-time analytics.

• Big Data Use Cases: ELT's scalable compute engines are designed for processing massive datasets efficiently.

• Dynamic Workloads: Organizations with fluctuating workloads benefit from ELT's ability to scale compute resources dynamically.

Future Trends

Artificial Intelligence, Machine Learning, and Data Mesh affect the future of ETL and ELT. Consider their impact in the details below.

Impact of AI and ML on ETL/ELT Processes

AI is already used in some data integration tools. This will increase further in the coming years. Meanwhile, ML will be common in transformation processes.

• Automation: AI-powered tools can automate transformation processes. This makes complex workflows in ETL and ELT pipelines easier to design or optimize during runtime.

• Data Quality: Machine learning models improve data cleansing by detecting anomalies and inconsistencies in real time.

• Predictive Analytics: AI enhances transformation by enabling predictive models to run on raw or structured data. This will integrate predictive insights into downstream processes.

Rise of Data Mesh Architecture and ELT

As a company grows and generates more diverse data, data warehousing solutions may not always be the best solution for analytics. A single team doing all the integrations may become a bottleneck as requirements become larger and more complex. A data mesh solves this problem by having different teams handle their own data domains.

• Decentralization: Data mesh shifts away from centralized architectures. Domain-focused teams will handle raw data ingestion and transformations, promoting ELT.

• Ownership: Data mesh principles allow domain teams to directly control their pipelines. This aligns well with the cloud-native and scalable nature of ELT.

• Interoperability: With a data mesh, ELT tools ensure compatibility across various storage and computation layers in distributed environments.

Predictions for the Next 5 Years

A lot can happen in 5 years. ETL and ELT, including tools, will evolve further to support data management needs. Here are some of them:

• Hybrid Solutions: Expect increased adoption of hybrid architectures where ETL and ELT coexist. This will be highly apparent in industries transitioning between on-premises and cloud environments.

• Serverless Processing: Tools leveraging serverless compute for transformation (e.g., AWS Lambda or Azure Functions) will dominate, offering elasticity and cost savings.

• Focus on Data Security: Both ETL and ELT frameworks will integrate stronger encryption, anonymization, and compliance techniques to protect sensitive data.

• Integration of Real-Time Pipelines: ELT will further evolve to handle streaming data pipelines, enabling seamless ingestion and transformation for real-time analytics.

Conclusion

ETL and ELT are both essential in an organization's data strategy. They are tailored for different use cases, architectures, and organizational needs. While they are different and have pros and cons, both transform raw data into actionable insights.

This article discussed that ETL is best for structured data, strict governance policies, and batch processing. ELT is a good choice when performance and agility matter to handle large amounts of data.

ETL and ELT will still have their place in the future which will be influenced by AI, ML, and data mesh principles. Choose tools and methods that can adapt to emerging trends.

Before committing to either ETL or ELT, evaluate your current data strategy and goals. Adopt a hybrid solution if necessary. Reach out to experts to get recommendations fit to the needs of the company.