ETL (extract, transform, load) has been the traditional approach for data analytics and warehousing for the last couple of decades. However, today we also have the option of ELT (extract, load, transform) an alternative approach to data processing. Ever since the inception of ELT, there has always been a debate on which is a better approach.

The aim of this blog is to end the ETL vs. ELT debate, once and for all.

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ETL vs. ELT: Showdown

ETL and ELT are both important data integration strategies with divergent paths towards the same goal—making data accessible and actionable for decision-makers. While both play a pivotal role, their fundamental differences can have significant implications for data processing, storage, and analytics.

Let’s explore what happens when the “T” and “L” are switched.

What is ETL?

Before choosing between ETL vs. ELT, it’s important to understand the meaning of each term.

So, what is ETL?

ETL has traditionally been an important step in the data integration process, which helps transfer data from disparate data sources to the target destination.

ETL begins with extracting data from different sources into a staging space. This data may not always uniform, and it is often in different formats. Transferring this data directly to the destination often leads to errors. So, it’s best to cleanse it and validate it, so only quality data reaches the end destination.

After transformation, the cleansed data is loaded into the specified destination(s).

ETL is essential in modern business intelligence processes as it makes it possible to integrate raw structured or unstructured data from different sources into one location to extract business insights.

Some people often ask the question, “Is ETL outdated?”

The answer to this depends on an organization’s needs, such as how many data systems they have in place, whether they need to transform this data, whether they need timely access to the compiled data, etc.

Before we delve deeper into when ETL is a better choice, first understand what ELT is.

What is ELT?

ELT’s meaning is quite different from ETL. The initial stage of ELT works the same way as ETL, which means raw data is first extracted from varying data sources. Unlike ETL, where data is first transformed before it is loaded, in ELT, data is directly loaded into the destination and then it is transformed within the destination such as a data warehouse.

The primary benefit of this approach is that data users can easily access all raw data whenever they need it.

It’s important to note that BI tools can’t use big data without processing it. So, the next step is to cleanse and standardize data. ETL warehouse normalizes the stored data for preparing customized dashboards and business reports.

Compared to ETL, ELT considerably reduces the load time. Moreover, ELT is a more resource-efficient method as it leverages the processing capabilities of the destination.

ELT is more suitable for cloud databases, storage platforms and data warehouses such as Snowflake or Amazon Redshift because these platforms have the capacity to store raw data in bulk.

ETL Process vs ELT Process

ETL Process 

An ETL process can be used for various use cases such as data migration, integration or just data replication.

Regardless, the basic process starts with data extraction, whereby data is extracted from disparate sources and then it is moved to a staging area for transformation. Now, there are various types of transformations that can be applied on this data, depending on the use case. For example, if the data is coming in from two different sources, a join transformation will combine it.

The data also has to be cleaned and validated before it is sent to the final destination.

Once that is done, it is finally loaded into the destination, which can either be another database, a warehouse. Users can choose from multiple options, mainly full load and incremental load. In full load, all the data is loaded at once, while in the second option, data is uploaded in batches.

This creates an organized pipeline, with a clear journey for the data from Point A to Point B.

ELT Process

The ELT process starts in the same way as the ETL, i.e., with data extraction. Once data is extracted, it is moved to a staging area, which can be a temporary storage location within the target system or a separate storage system. Staging is essential for data validation and to ensure data consistency before loading it into the target repository.

The next step is defining the schema for the data tables within the target repository. In this step, users have to create tables and define column data types. The data is then loaded into the target repository using tools and technologies, such as SQL-based loading scripts, data pipelines, or code-free ELT tools such as Astera Data Pipeline Builder.

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ETL vs. ELT Architecture: 8 Key Differences

Let’s look at some of the key differences between both approaches.

• Transformation Process

The order of the transformation process is a leading ELT and ETL difference. ETL approach processes and transforms data before loading it. Alternatively, ELT tools don’t transform data right after extraction. They instead load data in the warehouse as it is. Data analysts can choose the data they need and transform it before analysis.

• Data Size

A major difference between ETL and ELT is the data size. ETL warehouses work best with smaller datasets. However, ELT systems can handle a massive amount of data.

• Data Loading Time

ETL vs. ELT architecture also differs in terms of total waiting time to transfer raw data into the target warehouse. ETL is a time-consuming process because data teams must first load it into an intermediary space for transformation. After that, data team loads the processed data into the destination.

ELT architecture offers support for unstructured data. So, it eliminates the need for transformation before loading. So, users can directly transfer to a data warehouse, which makes ELT less time consuming.

• Data Analysis Time

Another ETL vs. ELT difference is the time required for performing analysis. Since data in an ETL warehouse is transformed, data analysts can analyze it without delays. But data present in an ELT warehouse isn’t transformed. So, data analysts need to transform it when required. This approach increases the waiting time for data analysis.

• Compliance

Cyber attacks affected 155.8 million US individuals in 2020 alone. To reduce the risk of data theft, businesses must follow CCPA, GDPR, HIPAA, and other data privacy regulations. This is why compliance is a critical factor in the ETL vs. ELT debate.

ETL tools remove sensitive information before loading it into the warehouse. As a result, this prevents unauthorized access to data. On the other hand, ELT tools load the dataset into the warehouse without removing sensitive information. So, this data is more vulnerable to security breaches.

• Unstructured Data Support 

Unstructured data support is another prominent difference between the ETL and ELT. ETL integration is compatible with relational database management systems. Hence it doesn’t support unstructured data. In other words, you can’t integrate unstructured data without transforming it.

ELT process is free of such limitations. It can transfer structured and unstructured data into the warehouse without hassles.

• Complexity of Transformation

Another difference is the transformation complexity. ELT approach enables moving large amounts of data to the target destination. However, you can’t push down certain advanced transformations, such as specific types of names or address parsing, to the underlying database. So, they must be performed in the staging server. At times, this can result in a “data swamp”. It’s a challenge to manually sort and cleanse this bulk data stored in one place.

The traditional ETL approach makes the process much simpler. It’s because you can cleanse data in batches before loading it.

• Availability of Tools and Experts 

From Astera Data Pipeline Builder to SSIS and Informatica PowerCenter, a myriad of different types of ETL tools are available in the market. Since this technology has existed for decades, businesses can make the most of these effective tools. But we can’t say this for ELT, a relatively newer technology. As a result, limited ELT resources and tools are available to meet customer needs. Furthermore, plenty of ETL experts are available in the market, whereas the ELT expert workforce is scarce.

ETL vs. ELT: Key Factors to Consider When Picking One

Choosing between ETL and ELT depends on several factors:

• Data Volume & Variety: ELT is better for handling large, diverse datasets, while ETL works well for structured data.
• Processing Speed: ELT enables faster ingestion, while ETL can be slower due to upfront transformations.
• Compliance & Security: ETL provides more control over data governance before storage, essential for regulatory compliance.
• Infrastructure & Costs: ELT leverages cloud scalability but may require more storage, whereas ETL reduces storage needs through pre-processing.

The table below provides some additional differences.

Industry-Specific Use Cases:

• Finance & Banking: ETL ensures strict compliance with regulatory frameworks like GDPR and PCI DSS.
• Healthcare: ETL is ideal for processing sensitive patient records, maintaining HIPAA compliance.
• E-commerce & Retail: ELT enables real-time customer analytics, leveraging cloud warehouses.
• Media & Streaming: ELT processes high-volume, unstructured data for personalized content recommendations.

ETL vs. ELT: Pros and Cons

Let’s take a look at a few notable pros and cons:

Benefits of an ETL Pipeline

• ETL pipelines are typically best for data cleaning, validation, and transformation before loading data into a target system.
• With ETL pipelines you can easily aggregate your data from multiple source systems into a single, consistent format.
• You can preserve current data source platforms without worrying about data synchronization, as ETL doesn’t necessitate the co-location of data sets.
• The ETL process extracts vast amounts of metadata and can run on SMP or MPP hardware that can be managed and used more efficiently without performance conflict with the database.
• ETL pipelines allow you to apply complex data transformations. If your data requires intricate business logic or significant changes in data structure before it’s usable, ETL provides a more controlled environment for these transformations.
• ETL significantly reduces the complexity and resource requirements of analytics.as transformations are applied before data is loaded, data into a target system.
• You can design ETL pipelines to handle both batch and real-time data integration, and leverage flexibility in data processing based on specific requirements.

 Drawbacks of ETL

• Maintaining ETL pipelines over time can be challenging. As data sources evolve and business requirements change, the ETL logic must be updated and tested regularly.
• If data quality issues are not detected and addressed during the ETL process, they can propagate to downstream systems, leading to incorrect analyses and decisions.
• There’s a risk of data loss or information being omitted if transformation rules are not carefully designed and tested.
• ETL process can be resource-intensive, requiring significant computing power and storage capacity, particularly for large datasets.

Benefits of an ELT Pipeline

• ELT allows you to be more flexible as you can store raw, unprocessed data in a data warehouse or data lake and use it for multiple purposes and analyses.
• The ELT approach prioritizes data loading over data transformation. As a result, data can be loaded into the target system quickly, making it available for analysis sooner.
• ELT is best for unstructured data as it uses schema-on-read approach, where you can ingest data without strict schema requirements
• ELT pipelines provide the foundation for advanced analytics, machine learning, and data science projects as they allow data scientists to access and manipulate raw data to create models and insights.
• ELT pipelines can simplify ETL (Extract, Transform, Load) processes by offloading complex data transformations to the target data warehouse.
• The best part about ELT pipelines is that there is no less risk of data loss since raw data is loaded into the target system directly.

Drawbacks of ELT

1. ELT pipelines may tie an organization to specific data warehousing solutions, potentially leading to vendor lock-in and limited flexibility.
2. Transformations are repeated for different analytical use cases, potentially leading to redundancy in data processing efforts.
3. Raw data loaded into a data warehouse may be less accessible and more challenging for business users and data analysts to work with, which means more efforts to create user-friendly views and transformations.
4. ELT pipelines often don’t include comprehensive data quality checks and transformations before data is loaded into the target system, requiring additional tools or support for data quality management.
5. ELT often relies on data warehousing solutions, which can be expensive to operate, especially when dealing with large datasets as storage costs, licensing fees, and infrastructure costs can quickly blow u

ETL vs. ELT: Which is the better data management strategy?

There is no clear cut “better strategy”. The approach you pick depends on your specific data management requirements. Here’s when ETL would be a better option compared to ELT:

1. There are Privacy Concerns:

You need to protect sensitive information before loading data to a destination. ETL reduces the risk of confidential information leaks. Moreover, it ensures that your organization doesn’t violate compliance standards.

2. Historical Visibility is Important:

Historical data provides a holistic view of business processes. From customers to suppliers, it offers detailed insights into stakeholder relationships. ETL is the ultimate choice for this purpose. It can help in preparing custom dashboards and precise reports.

3. Data is in a Structured Format:

If you’re unsure when to use ETL, determine the data’s nature. ETL is more suitable when the data is structured. While you can use ETL to structure unstructured data, you cannot use it pass unstructured data to the target destination.

4. You Need Historical Data:

You require a comprehensive audit trail and historical tracking of data changes, as ETL processes allow you to capture and log transformation activities.

5. Data Aggregation is Important:

Aggregating and summarizing data from multiple sources or at different granularities is a key requirement, as ETL allows you to create aggregated data sets during the transformation phase.

6. You’re Working with Legacy Systems:

You are dealing with legacy systems that require data transformations to meet the target schema.

On the flip side, we recommend using ELT when:

1. Availability of Data is a Priority:

You are working with large volumes of data, ELT is your best bet as it can load data into the target warehouse, whether structured or unstructured.

2. Data Analysts are ELT Experts:

Your organization has ELT experts since it is not that easy to find ELT experts as the technology is still evolving.

3. Budget isn’t a Problem:

ELT process enables you to load information without transformations. However, building an ELT pipeline can be more technical and expensive compared to ETL. An organization with a sufficient budget can go for this approach.

4. Raw Data Storage is Required:

You want to preserve raw, unaltered data for historical or future analysis, as ELT loads data into the target repository before transforming it, allowing you to maintain a record of the original data.

5. Scalability is Important to You:

You need to handle large volumes of data efficiently, as ELT can leverage the scalability of cloud-based data storage and cloud data warehouses resources for transformations.

6. Real-time or Near-real-time Processing is Required:

Your data processing requirements demand low-latency transformations or updates, as ELT allows you to load data as soon as it becomes available and apply transformations afterward.

7. Schema Changes Frequently:

You expect frequent changes to the data schema or structure, as ELT accommodates schema changes more flexibly since transformations are performed within the target repository.

8. Complex transformations are Involved:

Your data transformations are complex and require advanced processing, such as machine learning models or big data analytics frameworks, which ELT can support effectively.

ETL vs. ELT? How about ETL and ELT?

The ETL vs. ELT debate positions them as opposing data integration strategies, but many modern enterprises adopt a hybrid approach that leverages the strengths of both. This method enables organizations to optimize performance, cost, and scalability based on specific workloads, data types, and infrastructure.

How The Hybrid Approach Works

In a hybrid model, ETL is used for structured, mission-critical data that requires transformations before loading into a destination system, while ELT is applied to large-scale, raw datasets that benefit from cloud-based processing power. A typical workflow might look like this:

1. Initial ETL Processing – Data is extracted from operational systems (e.g., ERP, CRM) and undergoes critical transformations, such as data cleansing, enrichment, and standardization, before being loaded into a staging or intermediate database.
2. Cloud-Based ELT Execution – Large volumes of raw, unstructured, or semi-structured data (e.g., logs, IoT streams, social media feeds) are loaded into a cloud data warehouse (e.g., Snowflake, BigQuery, Redshift) and transformed using SQL-based processing or analytics tools.
3. Orchestration & Automation – A data pipeline orchestration tool ensures smooth execution, dynamically routing data through ETL or ELT based on business rules, performance needs, and cost considerations.

Use Cases for The Hybrid Approach

• Financial Services: ETL is used to transform regulatory reports before storage, while ELT enables real-time risk analysis on raw transactional data.
• E-commerce & Retail: ETL processes structured product and sales data for operational use, while ELT supports demand forecasting and customer sentiment analysis.
• Healthcare & Life Sciences: ETL ensures compliance with HIPAA and other regulations, while ELT facilitates advanced analytics on patient data for research.
• Manufacturing & IoT: ETL processes structured ERP data for reporting, while ELT helps analyze large-scale IoT sensor data for predictive maintenance.

Why Businesses are Adopting Hybrid ETL-ELT

1. Performance Optimization – Computationally intensive transformations occur in the most efficient environment, whether before or after loading.
2. Cost Efficiency – Leveraging cloud-native ELT reduces on-premises processing costs while using ETL for selective transformations minimizes cloud storage expenses.
3. Flexibility & Scalability – Organizations can adapt based on evolving data volumes, regulatory requirements, and technological advancements.

By blending ETL and ELT, enterprises achieve a balanced, efficient data integration strategy tailored to their needs.

Get the Best of Both Worlds with Astera Data Pipeline Builder

ETL and ELT prepare data for detailed analysis. No matter which method you opt for, Astera Data Pipeline Builder can meet your needs.

As an AI-powered, cloud-based, no-code data integration solution, Astera Data Pipeline Builder simplifies and streamlines ETL and ELT operations. Manage your ETL, ELT, and data preparation processes using intuitive, cloud-based features. Combine your data workflows, connect to 100+ sources, then create and automate workflows using simple commands in English.

With data preparation in the cloud and support for all latencies, Astera Data Pipeline Builder is a powerful asset for fast, reliable data integration. Set up a demo or speak to our team for more information.

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