ELT vs ETL Comparison

Choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) can be overwhelming. Both of them seem similar, but they have important differences that affect how you handle data for your organization.

Let’s look closely at both processes and their core differences so that you can figure out the best data integration method for your needs.

What is ETL (Extract, Transform, Load)?

ETL (Extract, Transform, Load) is one of the most common methods used in data integration. The process starts by extracting data from different sources, such as databases, CRM systems like HubSpot and Salesforce, or even spreadsheets. Its main job is to gather all this raw data, which might be in various formats and structures.

Once the data is collected from the source, the next step is transformation. This is where data is manipulated, cleaned, organized, and converted into a more useful format for analysis using a staging area and typically with a separate processing server. This might involve sorting, summarizing, or even merging data from different sources.

The final step in the ETL process is loading the transformed data into a target system, like a data warehouse. This is where your data is stored and made ready for use in business analysis, reporting, or other data-driven decisions.

Where is ETL used?

Many companies use ETL to prepare data for business intelligence tools. The data integration method helps create reports and dashboards that inform business strategies by providing clean and structured data.

In industries like finance and healthcare, ETL also helps maintain data in a way that complies with legal standards, especially when handling sensitive information.

Lastly, businesses often use ETL when they need to move data from one system to another. This is because it allows for a smooth transition and keeps the data intact and usable even when it is moved to a different environment.

What is ELT (Extract, Load, Transform)?

ELT (Extract, Load, Transform) is also a process used in data integration. Just like ETL, it can gather data from multiple sources. The main difference between the two methods is that ELT first loads the information to data warehouses or data lakes before transforming it.

Unlike ETL, where data is transformed on a separate processing server, ELT makes use of the processing power of modern data warehouses to transform data. ELT is particularly valuable when handling large volumes of data because of parallel processing. The process involves breaking down the data into smaller chunks and sending them to different nodes in the data warehouse for simultaneous transformation, which is faster and more efficient.

Where is ELT used?

Organizations that handle big data typically opt for ELT. It’s also beneficial when working with data lakes, as it lets you store raw, unprocessed data in its original format. This way, organizations can explore and derive insights from the data at a later stage without having to transform it upfront.

ETL vs ELT: Understanding The Core Differences

The main difference between ETL and ELT is the order by which they load and transform data. ETL is more sequential while ELT works in a parallel manner. Let’s dive deeper into the core differences between these two data integration methods:

Data handling process

With ETL’s sequential process, data must go through each step, which leads to a longer waiting period to make it available for analysis. It also requires significant computational resources since you’ll need a separate server for the transformation process.

Additionally, ETL processes can become more complex and resource-intensive when dealing with unstructured or semi-structured data, which is increasingly common in today’s data environments.

Meanwhile, ELT’s use of parallel processing allows for more efficient handling of large datasets. Modern data warehouses used in this method can handle massive amounts of data and complex transformations, so you can get accurate data quickly.

Architecture and infrastructure

Since ETL needs a separate staging area, you’ll have to set up additional infrastructure to accommodate the transformation process. As such, it involves setting up servers and databases specifically for this purpose. Depending on your organization’s needs and resources, the server can be a physical server or a virtual machine.

On the other hand, ELT, especially in modern, cloud-based systems, uses the power of the data warehouse for in-place transformation. This usually leads to a simpler setup compared to ETL. If you already have a robust infrastructure, it can work with your existing data warehouse or data lake so that you can save time and resources.

Performance and scalability

Many organizations opt for ETL when handling structured data and smaller datasets. It can provide reliable and consistent performance through batch processing. However, it may face challenges when dealing with large volumes of data, which your organization may eventually need to handle.

Conversely, ELT is designed for scalability. It can work with large datasets and unstructured data with ease. When it comes to performance, ELT does depend largely on your data warehouse’s capabilities. As such, it doesn’t guarantee that ELT will always be faster than ETL.

Data transformation capabilities

ETL provides extensive and complex transformation capabilities, which is ideal for scenarios that need detailed and intricate data manipulation. For example, you can use ETL to perform complex calculations and generate derived fields based on multiple data sources.

ELT is more suited for straightforward transformation needs since it relies on the processing power of the data warehouse. It can handle common tasks like data filtering, aggregation, and basic data cleaning for large data volumes, but it doesn’t offer as many built-in transformation capabilities as ETL.

Real-time data processing

If you need real-time data processing for quick decision-making, you should opt for ELT, which offers almost immediate data availability. Parallel processing breaks real-time data streams into smaller chunks, which allows ELT to process information simultaneously. As a result, you can get up-to-date insights and make timely decisions based on current trends.

ETL takes more time to get data ready for use because it has to complete every step before the data becomes available for analysis. To be fair, ETL does provide the advantage of maintaining data quality and consistency through its sequential transformation process.

Should You Choose ETL or ELT? 

Deciding between ETL and ELT is more than just weighing the differences in their data handling processes, architecture, performance, and transformation capabilities. It’s more about determining how they fit with what your business needs. Let’s look at some key factors to consider when making this decision:

Your organizational data needs and goals

Think about how much data you have or want to deal with each day. Do you have plans to scale up and handle larger volumes of data in the future? The amount of information you work with can help you find the right integration method for your data governance strategy.

Additionally, you’ll also want to consider the different data types that you interact with. Do you primarily deal with structured data, or do you also handle unstructured or semi-structured data? ETL is better with the former, while ELT shines with the latter.

Aside from volume and variety, you must figure out the velocity or how fast data comes into your systems. This factor can help you decide whether you need real-time parallel processing or batch processing.

Moreover, your choice should support your business objectives. Whether it’s improving decision-making with real-time data or managing large datasets, pick the process that aligns with your goals.

Consider your current data needs and what you might need in the future as well. This way, you choose a method that can grow with your business.

Performance and efficiency

Researching how ETL and ELT perform in different situations can also help you figure out the best method. For example, ETL is better for detailed data transformations and generating derived fields.

Let’s say you’re in the retail industry, and you have a large dataset containing sales information from different stores across the country. You want to calculate the total revenue for each store, as well as the average revenue per customer.

In this case, using ETL, with its complex transformation capabilities, lets you extract the sales data from each store, transform it by multiplying the unit price by the quantity sold to get the total revenue, and generate the average revenue per customer.

Meanwhile, ELT could be more efficient for quickly handling large amounts of data. For example, if you’re in marketing and want to analyze customer behavior by collecting data from Facebook, Instagram, and TikTok, you can use ELT to collect raw data from each platform and store it.

Raw data could include post content, engagement metrics (likes, comments, shares), user demographics, and sentiment scores. You can then perform the necessary transformations, like aggregating engagement metrics to calculate average engagement rates.

Consider how each process affects your system’s performance. Again, ETL might require more resources for data transformation, while ELT could be more streamlined.

Cost differences and implications

The costs can vary depending on the size of your business. As mentioned, ETL needs a separate staging area, which entails additional server costs. Generally, cloud-based ELT is a pay-as-you-use or subscription model, which can be cost-effective. 

Think about the return on investment (ROI) for both ETL and ELT. Which one offers more value in the long run? We can’t speak for your organization, so you’ll have to decide based on your specific needs and budget.

Consider the costs for initial setup, ongoing maintenance, and any additional expenses associated with each method. ETL may need more upfront investment in hardware, software, and IT workforce to design and maintain its complex processes.

On the other hand, the ELT process is more cost-effective than ETL since you’ll only need to pay for cloud-based services. However, keep in mind that as your data volumes grow, the cost of storing and processing that data in the cloud may also increase.

Integration with existing systems

Check how well ETL or ELT integrates with your existing IT setup, including any legacy systems or integration layer tools you may be using. Are there any specific requirements or constraints you need to consider? Is there a need for compatibility with certain databases or data sources?

For instance, the cloud-based data warehousing platform Snowflake supports both ETL and ELT. It has built-in connectors for various data sources and easily integrates with popular data integration tools like Informatica and Talend.

ETL can work and connect easily with traditional data warehousing systems and relational databases, such as Oracle, SQL Server, or MySQL.

Meanwhile, ELT is more flexible and can handle both structured and unstructured data. However, when it comes to integrating with existing systems, it may require more testing and tweaking of the settings due to its direct load approach.

Compliance and security features

Consider any compliance requirements your business has. In healthcare, hospitals and clinics deal with sensitive patient data that they should handle according to privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States.

With ETL, these organizations can implement data transformations and cleansing processes before loading the data into their data warehouse or analytics platform. This allows them to keep the data properly anonymized, encrypted, and masked to protect patient privacy.

For instance, when extracting data from electronic health records (EHR) systems, ETL can be used to remove personally identifiable information (PII), such as patient names, addresses, and social security numbers, while still retaining necessary clinical information for analysis.

ETL allows you to control the data before loading it into the target system. As a result, healthcare organizations have the flexibility to implement data security measures and maintain integrity and confidentiality.

ELT also offers its own set of compliance and security features. Using this method, you can apply security measures at the database level, including implementing user access controls, encryption, and data masking. 

How Important are Data Warehouses in ETL and ELT?

Data warehouses do more than just store information. They have a central role in both ETL and ELT processes, as well as how you can manage and use data effectively. Here’s how data warehouses impact both ETL and ELT processes.

ETL

In ETL, data warehouses serve as the final destination in the sequential process. Once data goes through the extraction and transformation stages, it’s loaded into the data warehouse, where it’s stored and managed for future use.

We’ve been saying that the capabilities of your data warehouse influence the ELT process. Well, it also has an impact on the ETL design, specifically with how the transformation logic is set up and the staging area’s design.

Even when it comes to considering the ETL data integration method, you still need to consider what your data warehouse can handle in terms of storage capacity and processing power.

ELT

Data warehouses are a particularly important platform in ELT because it’s where transformation happens. The warehouse’s features and capabilities influence how fast and efficiently the system can perform the necessary transformations on the raw data.

Modern data warehouses can significantly enhance the performance of ELT processes. They’re built to handle large and diverse datasets efficiently, so you won’t have to worry about performance bottlenecks. Additionally, it also provides you with the ability to scale, which is crucial if your business deals with big data.

Operational implications

Whether you go with ETL or ELT, you still need to consider the regular maintenance and management of your data warehouses. ETL might involve more complex data pipelines, while ELT could place higher demands on the warehouse’s processing power. Both lead to different cost implications and resource allocation needs.

Key Takeaway

In the ETL vs ELT debate, you have to consider their unique strengths and roles when it comes to data integration. Choosing between these processes should be about finding which method aligns best with your organization’s specific needs. Whether you choose ETL or ELT, the goal is to make your data work effectively for your business.