ETL Vs ELT: Key Differences, Advantages and What to Choose?

What is ETL?

ETL: Extract, Transform, Load

ETL and Data Warehouses

OLAP  (Online Analytical Processing) data warehouses require data in a relational format, making ETL essential for transforming and mapping data before loading to ensure accurate analysis.

Structured Data Flow

ETL follows a well-defined process of extracting data from various homogeneous or heterogeneous sources, transferring it to a staging area for cleansing, enrichment and transformation, finally loading it into a data warehouse for analysis. This ensures data is refined and ready for business insights.

Traditional Challenges

Legacy ETL processes often require significant effort, involving extensive planning, manual coding, and continuous oversight by data engineers. Updating workflows and integrating new data sources can be time-consuming and resource-intensive.

Modern ETL Solutions

The emergence of cloud-based ETL tools has streamlined the process, reducing the need for manual development and accelerating data integration. These platforms enable faster, more efficient pipeline management while simplifying transformation logic and enhancing collaboration.

Scalability and Flexibility

With modern ETL solutions, organisations can quickly integrate data from multiple sources without heavy infrastructure dependencies. This ensures a scalable and flexible data processing approach, especially for cloud based data warehouses.

ELT: Extract, Load, Transform

What is ELT?

ELT is a data integration process where data is first loaded into a storage solution before being transformed. Unlike traditional ETL, ELT eliminates the need for a staging area, allowing structured, semi-structured, and unstructured data to be directly loaded into cloud-based storage, such as Azure Data Lake. The transformation is then executed within the compute layer of modern data warehouses like Databricks or Microsoft Synapse, which are designed to separate compute from storage. This architecture enables scalable, high-performance processing while maintaining flexibility in data management.

Data is stored in the lake, but the transformation is performed in Databricks. This separation allows for optimised resource utilisation, ensuring efficient data processing without compromising storage management.

ELT: Extract, Load, Transform

ELT and Data lakes

The ELT  process is closely aligned with data lakes, enabling seamless storage and processing of vast amounts of structured and unstructured data. Unlike traditional OLAP data warehouses, data lakes allow raw data to be directly loaded without prior transformation. The data transformation, cleansing and enrichment processes occurs before the data is prepared for analysis. 

Here are key aspects to understanding about the relationship between ELT and data lakes:

Powered by Cloud-Based Infrastructure

ELT relies heavily on modern cloud-based servers with scalable storage and high-speed processing. Platforms like Databricks, Azure Synapse and Microsoft Fabric Data Warehouse  make ELT possible due to their exceptional data processing capabilities, eliminating the need for traditional staging areas.

Unrestricted Data Ingestion

With ELT and data lakes, organisations can ingest vast volumes of raw data , regardless of format or structure. This flexibility allows businesses to store and access continuously incoming data without requiring upfront transformations.

On-Demand Data Transformation

ELT only transforms data when needed for specific analysis, providing the flexibility to customise transformations according to the desired outcome. This enables businesses to generate diverse metrics, forecasts, and reports without modifying the entire data pipeline, as is required in ETL.

Limited Use Cases Compared to ETL

ELT is powerful and scalable, but it’s not always the best fit for every situation. Since transformations happen after loading, it can sometimes slow down analysis if not optimised well. Also, ELT tools are still evolving, so for very complex data needs, some organisations may find traditional ETL more reliable. That said, with the right setup, ELT can be a great choice for modern data processing.

ELT combined with data lakes offers unmatched scalability and flexibility in handling raw data, but it may require specialised skills and infrastructure to fully leverage its potential.

ELT's ability to directly load raw data without pre-transformation makes it an ideal choice for businesses prioritising speed, flexibility, and scalability.

ETL and ELT Cost: Factors to Consider

Below are some major cost factors to be considered: 

Infrastructure and Hardware Costs

ELT offers a more economical solution for large-scale data by storing raw or interim data in cost-effective storage like Azure Data Lake Storage (ADLS). Unlike traditional ETL, which relies on expensive databases for intermediate datasets, ELT minimises storage costs and optimises processing expenses, making it a budget-friendly choice.

While ETL can be costly, its expenses can be reduced by utilising cost-efficient processing options like Kubernetes and AWS Lambda. These solutions offer scalable, pay-as-you-go computing, ensuring resources are used efficiently. By dynamically scaling based on workload demands, businesses can lower operational costs while maintaining performance in their ETL processes.

ELT relies on scalable cloud storage and in-warehouse transformations using platforms like Snowflake and Synapse. While cloud object storage, such as data lakes, is cost-effective and common to both ELT and ETL, the compute costs associated with in-database transformations can rise significantly as data complexity and transformation workloads increase over time.

This makes compute resource usage a critical factor in managing ELT costs, independent of the storage expenses. Efficient compute management becomes essential to ensure cost optimisation in ELT environments.

Processing Costs and Scalability

ETL processes data before loading, requiring a separate environment that adds complexity but allows for cost optimisation. ELT shifts transformation to the destination system, simplifying architecture but potentially increasing costs, especially for complex or frequent operations.

Scalability impacts costs significantly. ETL is often more cost-effective for moderate data volumes, but as data grows or latency decreases, ELT costs can rise rapidly, especially with full data reprocessing. Long-term growth projections are crucial in cost evaluations.

Labor and Implementation Expenses

Modern data tools streamline data modelling and support low-code development, significantly reducing labour and implementation time. They simplify orchestration through integrated environments, allowing seamless interoperability between tools. This eliminates the need for separate transformation layers, making the entire data pipeline more efficient and easier to manage.

ELT empowers analysts to manage transformations directly within the data platform, reducing reliance on specialised engineering teams and lowering operational costs.

By unifying this data engineering approach, development cycles accelerate, leading to further cost savings, particularly in staffing. Platforms like Databricks and Fabric enhance this efficiency by offering built-in version control, documentation support, and monitoring capabilities. These features mitigate maintenance challenges and ensure a well-governed data environment.

Long-Term Maintenance Considerations

ETL systems built by experienced staff tend to be more maintainable, reducing long-term costs despite higher initial investment. Their structured approach simplifies troubleshooting and enhances stability.

ELT, however, can become costly if transformation logic grows complex or poorly documented, especially with high staff turnover or weak governance.

Additionally, ETL ensures cleaner, validated data upfront, minimising downstream issues, whereas ELT’s raw data storage risks create difficult to manage "data swamps."

Industry-Specific Cost Considerations

ETL suits highly regulated industries like finance and healthcare, ensuring strict data validation and compliance through structured architecture.

In contrast, ELT is preferred in data-driven sectors such as retail, manufacturing, telecom, and technology, where rapid decision-making on large, unstructured data is crucial.

ELT thrives in cloud-native environments where compute and storage are decoupled, offering scalability and cost optimisation. Platforms like Databricks and Microsoft Fabric enhance this with real-time processing capabilities and minimal infrastructure management, making ELT a cost-effective choice for large-scale operations.

Cost Optimisation

Cost efficiency also depends on tool selection, with combinations like Airflow + Prophecy and Databricks optimising performance and expenses. As ETL/ELT tools evolve, cost dynamics may continue to shift.

ELT or ELT: What to Choose?

Choosing between ETL and ELT depends on various factors, here's a breakdown:

Data Volume

If you deal with large volumes of data including huge unstructured or semi-structured data, ELT would be the best choice. ELT is a more suitable solution due to its scalability and ability to handle diverse data types efficiently for large volumes of data.

Smaller volumes of structured data can be processed using ETL where data quality is more important than scalability.

Processing Capabilities

Processing capabilities play a vital role in selecting the right method, however, this highly depends on the target system. For example, a data warehouse with robust processing capabilities, ELT is for you.

Whereas a target system with inefficient processing power, ETL may be required to perform transformations on a separate server.

Data Quality and Compliance

Where data governance and quality standards are crucial, ETL is the most suitable solution. Quality checks and data cleansing are performed before the loading takes place. While ELT is suitable for industries where data quality and governance can be conducted after loading, which can be resource intensive.  

The decision ultimately hinges on the specific data architecture, regulatory requirements, business goals and processing capabilities.

ETL Vs ELT

Conclusion

Choosing between ETL and ELT depends on your organisation's data structure, processing needs, and infrastructure. ETL ensures high data quality and compliance, making it ideal for structured data, while ELT offers speed, scalability, and flexibility, which is best suited for large, unstructured datasets.

Many modern organisations use a hybrid approach, combining ETL for legacy systems and ELT for cloud-native architectures.

Understanding these differences will help you align your data strategy with your business goals, ensuring optimal performance and efficiency.

At Cloudaeon, we specialise in optimising ETL processes across platforms like Databricks, Azure Synapse and Micrsoft Fabric to ensure that your data pipelines run smoothly, efficiently and cost-effectively.