ETL vs ELT: Understanding Key Differences for Effective Data Integration

According to a research report* by MarketsandMarkets, the data integration market is expected to grow from USD 11.6 billion in 2021 to USD 19.6 billion by 2026. This implies the huge potential of data integration and the two approaches to data management—ETL and ELT. Today, we’ll discuss their key differences and their use cases. However, in the battle of ETL vs. ELT, choosing one over the other depends on the technicalities of business use cases for transforming data.

What is ETL?

ETL– “Extract, Transform, Load”. You can integrate your data from multiple sources into a centralized data warehouse. Here’s how it works:

1. Extract: Data is extracted from various sources, such as databases, CRM systems, or flat files such as XLSX. The raw data is pulled out from its sources in this step.
2. Transform: Once the data is extracted from your source, it’s transformed into a suitable format or structure for the target data warehouse. This might involve cleaning the data, filtering out duplicates, or applying business rules.
3. Load: The transformed data is loaded into your target data warehouse and ready for analysis and reporting.

ETL is handy when dealing with on-premises data warehouses and applying complex transformations before loading your data. 

While ETL allows you to control the amount of data loaded to your destination by loading just the transformed data, it is not suited for situations where a very high volume of data is needed in near real-time. 

The process of transforming the data before loading it to the destination makes data availability extremely slow as the volume of data grows. More complex transformations are required to accommodate different types of data, which has led to more businesses adopting ELT for their data integration needs.

What is ELT?

ELT– “Extract, Load, Transform” is a modern approach that leverages the power of cloud computing and big data technologies. Here’s how it differs:

1. Extract: Like ETL, extracting data from various sources is the first step.
2. Load: However, unlike ETL, the data is immediately loaded into your destination in its raw form.
3. Transform: The transformation happens inside your destination. This approach takes advantage of modern cloud platforms’ processing power and scalability.

ELT takes advantage of the scalability and performance of cloud-based platforms, allowing for quicker data loading and leveraging the processing power of the target system to handle transformations. 

The shift to ELT in recent years is primarily a result of the adoption of cloud data warehouses and data lakes, which have brought the cost of data storage significantly down. ELT integrated with cloud data warehouses allows you to store unstructured data and perform fast and large-scale data transformations.

ETL vs ELT: What’s the Difference?

Now that we have a basic understanding of ETL and ELT let’s dive deeper into their differences. Here’s a side-by-side comparison to illustrate the key distinctions:

Parameter	ETL	ELT
Process Order	Extract → Transform → Load	Extract → Load → Transform
Data Transformations	Before Loading into the Destination	After Loading into the Destination
Data Volume	Better for smaller datasets	Optimized for larger datasets
Complexity	It is more complex to manage transformations before loading	Simplifies loading but needs powerful infrastructure for transformation
Storage	Requires a separate staging area for transformed data	Utilizes the destination for loading the raw data
Speed of analysis	ETL is a time-intensive process and is slower than ELT	ELT is faster by comparison due to immediate loading and powerful transformation capabilities
Type of Transformations	For Complex transformations	For Simpler transformations
Maturity	In use for more than 20 years	It is a relatively newer form of data integration
Supported Data Types	Relational or structured data format	All data formats are supported, whether structured, semi-structured, or unstructured.
Maintenance	The secondary processing server adds to the maintenance burden	With fewer systems, the burden is reduced
Costs	Platforms provide scalable plans that can change with data ingestion. On-site ETL solutions for businesses are more expensive.	Platforms provide customizable plans with affordable choices for smaller-scale transformations.
Security Compliance	Easier to satisfy GDPR, HIPAA, and CCPA compliance standards	Sometimes, it tends to violate GDPR, HIPAA, and CCPA standards. But, it is easier with tools like Hevo Data, which already complies with them.
Use Case	Ideal for financial reporting and legacy systems	Great for big data analytics and real-time processing
Future Trends	It is still relevant but evolving	Rapidly growing with the rise of cloud computing and big data technologies

When to use ETL vs ELT?

ETL vs ELT depends on several factors, including data volume, infrastructure type, and specific use cases. Let’s consider some scenarios in which each method might be more appropriate.

When to use ETL?

• Use Case 1: Financial Reporting

Data accuracy and integrity are critical in a financial institution. ETL is ideal here since it allows extended data validation and cleaning before loading into a data warehouse. Therefore, reports produced in this state will be of high quality and contain reliable data.

Example: You work in a bank and must consolidate data from different sources to perform the month-end reporting process. ETL would facilitate extracting data from each system in every branch, transforming the data to consistency—for instance, in systematizing account types—and consequently loading it into your central data warehouse. That way, these reports will be accurate and trustworthy.

• Use Case 2: Legacy Systems Integration

ETL in legacy systems can bridge the gap between old and new technologies since, during the loading process, the transformation guarantees that the data will be compatible with modern Data Warehouses.

Example: By using ETL, you would be able to extract data from an old CRM if it has been used for several years and your company is migrating to a new one, clean and transform it according to the structure of the new system, and load it into a new CRM. This will provide a smooth transition and maintain data integrity.

When to Use ELT?

• Use Case 1: Big Data Analytics

Organizations dealing with massive datasets go for ELT. New-generation, cloud-based data warehouses and data lakes are competent enough to handle vast volumes of raw data efficiently, creating a quick and flexible transformation.

Example: Suppose an e-commerce company is working with customer behavior data. Using ELT, you can load every source of raw data—tap stream information and buy history—and the user profiles directly into a cloud data warehouse. Run transformations and analyses directly inside the warehouse now, and the processing power comes right to bring insights quicker, almost in real-time.

• Use Case 2: Real-time Data Processing

ELT can be most effective if real-time data processing is required due to its capability for fast data loading and further transformation if needed.

Example: Suppose you run a real-time recommendation widget on a streaming service. With ELT, you can stream raw user interaction data directly into a data warehouse. In generating recommendations, such transformations can happen at query time, so users get the most relevant suggestions.

Which is better: ELT or ETL?

The rise of cloud data warehouses has opened up new possibilities in data integration, making the choice between ETL and ELT more relevant than ever. But ultimately, the best method depends on your specific needs and circumstances.

While ELT may offer many benefits, especially in speed and scalability, some teams may need to stick with ETL depending on their deployment because they are working on old systems or must adhere closely to rules governing how information should be handled.

ETL’s structured data pipeline secures the management of sensitive data and aids in adequate adherence to compliance requirements, especially where data integrity and quality are essential.

Irrespective of your choice, you need a robust data integration platform to meet such needs as success. Suppose you go for the conventional ETL mode or a contemporary ELT approach. In that case, such platforms will help them by facilitating efficient strategies implementation to achieve their objectives.

How can Hevo Data support your ETL and ELT requirements?

So much in the data integration domain is decided by the choice of a platform; thus, choosing your data pipeline platform wisely is very important. Hevo Data, a no-code data pipeline platform, supports ETL and ELT processes, making it flexible and powerful for dealing with various data needs. Here’s how Hevo Data comes in handy for this:

• Seamless Integration: Hevo Data supports over 150 data sources, so extracting data from anywhere becomes easy. Hevo Data sets you up for a smooth and reliable extraction process for databases, SaaS applications, or flat files.
• Strong Transformation Capabilities: It has robust transformation tools for ETL processes. You could clean, enrich, and transform your data before it’s loaded into destinations. In this way, only high-quality structured data can enter the data warehouse and get ready to be analyzed.
• Efficient Loading: Hevo Data optimizes the loading process to load data efficiently. Whether using ETL or ELT, Hevo Data ensures that data is loaded to your data warehouse or data lake quickly and accurately. This efficiency is critical for real-time data processing and big data analytics.
• Scalability and Performance: Hevo harnesses cloud infrastructure’s power to provide the scalability you need for large datasets. Besides, the architecture is designed to hold vast volumes of data without affecting speed, making your integration processes fast and reliable.
• Flexibility for ELT: If you prefer the ELT method, Hevo Data also provides a way to load the raw data directly into the data warehouse for transformations there. Such flexibility is ideal in modern, cloud-based data environments, where post-load—rather than pre-load—transformations can often be more efficient and agile.
• User-Friendly Interface: Hevo Data’s user-friendly interface makes setting up and managing your ETL/ELT processes easy. You can create and maintain data pipelines with minimal hassle.

Conclusion

To wrap things up, let’s highlight some of the critical differences between ETL and ELT:

• Process Definition: ETL stands for Extract, Transform, and Load, whereas ELT means Extract, Load, and Transform. Both methods serve the purpose of data integration but differ in their approach.
• Data Movement: ETL moves the data from your source to a staging area and your target destination. There is no staging in ELT; data is loaded directly into the destination, where transformations occur. 
• Transformation Location: ETL transforms data before loading it into a destination. This is best for data privacy and compliance since it allows for the upfront cleaning of sensitive data. For performing transformations, the ELT utilizes the resources of the destinations, discarding the need for staging.
• Flexibility and Speed: ELT offers greater flexibility and efficiency, especially when dealing with large volumes of unstructured data. It provides immediate access without waiting for transformations.

FAQs

Q1. When might you want to use ELT over ETL?

This flexibility and ease in handling new and unstructured data gives ELT an edge over ETL. With ELT, any information is stored as it comes in if there is no time or capability to transform and structure it immediately. You get instant access to all your data whenever needed without waiting for the transformation process.

Q2. Is ELT cheaper than ETL?

ELT utilizes the data warehouse to handle basic transformations directly, eliminating the need for data staging. On the other hand, ETL can perform more complex data transformations and is often more cost-effective than ELT.

Q3. Does Snowflake use ELT or ETL?

Snowflake supports both ELT and ELT processes. It is a variant of the flexible options that allow you to use easy ETL and ELT methods or avoid ETL altogether to focus more closely on critical data strategy and pipeline optimization projects.

Q4. What are the ETL tools?

Some open-source tools include Singer, Apache Airflow, and Hadoop. To bypass the manual method and save yourself from the hassle, you can use applications like Hevo Data or AWS Glue.

Q5. Is Reverse ETL the same as ELT?

Reverse ETL is not ELT. Your cleaned and processed data is taken from your destination and returned to business applications such as Salesforce or HubSpot. This will eliminate data silos, enriching business operations and forecasting with clean data available for operational systems.

Share your experiences about learning ETL vs ELT in the comment section below. Do you have any further queries on ELT vs. ETL? Post them. We’d be happy to help.

References:

*Data Integration Market Research