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ETL vs. ELT: What Are the Key Differences?

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The industry 4.0 revolution is centralized around how we collect, analyze, and ultimately use our data. But how does this process work? How are most companies hoping to make their needed data-backed business decisions in the foreseeable future? Well, that boils down to their ETL and ELT processes. That is why this blog discusses these two main methods of gathering and analyzing data to be used for decision-making, as well as the pros and cons of each.

What is ETL?

ETL is a process that allows you to extract large amounts of data from one system, transform it into the form you want, and then load it into another system. ETL stands for “extract, transform, and load.”

ETL is used in many businesses to move information from one place to another. For example, suppose you have a customer database on your website but want to move that information into another application like Salesforce. In that case, ETL will help you do so without having to enter every record into each program manually.

ETL also makes it possible for companies to use multiple platforms at once without having to duplicate their efforts. If you’re managing multiple websites and need access to the same set of user data across all of them, ETL can help automate that process for you.

The ETL Process 

Extract, Transform, and Load (ETL) transforms data from one format to another. The three steps in this process are extraction, transformation, and loading.

  • Extraction is the first step in ETL. This step uses the extractor tool to extract data from the source system and store it in a staging area. The data is removed from its source, usually a database or flat file.
  • Transformation is the second step of ETL, where the data is transformed into a new format that works better with its destination system. It could mean changing the data layout into a form that fits better with its destination system or converting numbers into text strings so they can be used by applications that require text-based data instead of numbers.
  • The final step of ETL is loading which occurs when data is moved from its source to its destination system. Data can be loaded directly into a database or a staging area before being loaded into its final destination database.

What is ELT?

ELT is a common approach to data warehousing and stands for Extract, Load, and Transform. 

In ELT, you take data from the source, load it into a data warehouse, and then transform it into something more usable for analysis.

However, there are some things that you could improve with this approach compared to ETL. 

Traditional ETL requires a lot of time and effort to execute, leading to performance issues during the transformation process.

Because of this, many companies are turning to cloud-based data warehousing solutions that use ELT as a starting point but add additional capabilities.

The ELT process 

The Extract Load Transform (ELT) process is a three-step process that involves extracting data from a source, loading the data into a target system, and transforming it into a target format. 

The ELT process can be considered a “three-legged stool,” where each leg represents one of the three steps.

  • The first leg is the extract step, which involves extracting data from the source systems. It can be done using one of two methods: direct extraction or via an interface. Direct extraction involves querying for specific records, while interfaces allow you to access all available data without having to query for individual records.
  • The second leg is the load step, which involves moving transformed data into your final destination system, such as RDBMS or NoSQL database. It can be done by simply copying the changed files or using an ETL (Extract Transform Load) tool like Microsoft SQL Server Integration Services (SSIS).
  • The third leg is the transform step, which involves transforming extracted data into the desired format. It can include changing dates to strings or integers, changing names, or removing duplicate entries. You may also want to add new information or rename fields to make them more meaningful in your target application.

Key differences between ELT and ETL 

When you’re evaluating ETL and ELT solutions for your data warehouse, there are two primary ways in which these processes differ. 

  • One is where the data transformation occurs—either on a different server or within the data warehouse itself. 
  • The other is whether raw data is transferred into the data warehouse or transformed on its way to being stored.

ETL is a method for getting data from point A to point B by focusing on the process itself and emphasizing the transformations.

ELT is all about the destination it’s about what happens between point A and point B: how your data is cleaned and filtered as it winds its way through your pipeline.

When using ETL, your ability to transform data depends on where you get it and where you’re sending it. If you need to cleanse or filter sensitive information before sending it off, then ETL is probably right for you. 

On the other hand, if you’re moving raw data around without manipulating it at all (or very little), then ELT might be better suited to your needs because it allows for faster ingestion by delivering data directly into its target system rather than needing to go through another server first.

ETL transforms information on a separate processing server, while ELT transforms data within the data warehouse. You’ll need to ensure that your ETL solution is compatible with your existing infrastructure and any plans you have for expanding your infrastructure.

ETL doesn’t transfer raw data into the data warehouse; instead, it sends transformed information directly to where it needs to go. If you want to save time by sending raw data straight into your database without having it undergo any transformation, then an ELT solution may be better suited.

History of the ETL & ELT Processes

The evolution of ETL & ELT processes has been long and complicated.

Today, businesses can integrate their data from multiple sources and perform unlimited SQL queries directly in their cloud data warehouses.

For decades, technology has been used to store information for later use. But it wasn’t until the late 1960s that disk storage allowed access to data anytime without re-entering it by hand.

DBMS (Database Management Systems) was pioneered by IBM and various companies shortly after. The development of networking technology soon led to data sharing between computers.

Since the 1970s, ETL has been helping businesses overcome the challenges of data integration.

ETL became increasingly crucial in integrating various data sources with enterprise computing systems.

With the advent of data warehouses in the 1980s, ETL became more vital for businesses that needed to integrate data from multiple sources.

In the 1990s, ETL solutions became more affordable for mid-enterprises.

As cloud computing emerged in the 2000s, cloud data lakes and warehouses caused another evolution: ELT (Extract Load Transform).

ELT is a streamlined approach to data integration that allows you to extract and load data from any source or format seamlessly, no matter the size or complexity—and then transform it into a unified form for analysis.

Solutions for ETL Data Transformation with Datorios

Transform your data with Datarios’s data transformation toolkit.

Datarios’s data transformation solution allows you to transform data in various ways according to your business needs. 

Utilize the correlator to easily match up un-synced events, meeting defined conditions and joining them for further transformation along the pipeline. Correlations can be used for data enrichment, multi-source joining, and state-based filtering. 

Replace manual coding with code capsules by using Datarios flexible coding, which allow you to create automated enriched events; a script can trigger that. In addition to supporting all workloads and providing better data observability, code capsules seamlessly integrate calculations, algorithms, and machine-learning models into any pipeline.

You can log and record pipeline errors using a multi-filter, while distributors will dynamically route process events.

Using mappers, you can easily create conditional values based on predefined conditions within meta-data or circumstances by transforming fields according to any initial schema loading.


With a cloud-based ETL data pipeline solution, you can fully leverage all the benefits of cloud computing: elasticity, cost-efficient scalability, and endless flexibility.

You’ll have access to a highly scalable infrastructure that can run in minutes. No need to invest in new hardware!


How to test the ETL process?

The ETL testing process consists of five steps:

  1. Identify your business requirements. 
  2. Assess your data sources. 
  3. Create test cases. 
  4. Begin the ETL process with the extraction. 
  5. Perform the necessary data transformation and load the data into the target destination, documenting your findings.

What is an ETL process in a data warehouse?

An ETL process is an extraction, transformation, and loading process.

In other words, it’s how you get the data from your source systems into the data warehouse.

What is data transformation? 

Data transformation is the process of converting data into a different format. It can be done for various reasons, including to make it easier for humans to understand or to get it into a form compatible with another computer system.

What is data transformation in the ETL process? 

Transformation is the process of cleansing and aggregating data so you can analyze it.

It can be a complex process, depending on the data type, the tools you’re using, and the purpose of the analysis.

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