Introduction
The processes of ETL (extract, transform, load) and ELT (extract, load, transform) are similar because the steps involved serve the same protocol. The main difference between the processes is the order in which these processes take place.
Before we go into detail about how each process, it is important to understand what each step of this process entails—let’s look closer.
Extraction
In both ETL and ELT, the first process is known as extraction. During this stage, raw data is collected from the selected sources.
This data could be collected from SaaS applications, various IoT sensors, manual inputs, and other various data sources. The collected data could be in a variety of formats, structured, semi-structured, or unstructured—it all depends on the sources involved.
Transformation
This is the second process in the ETL protocol—it’s also the third process in the ELT protocol. But what is it?
This is the stage in which raw data is converted into a usable form for your business. Data that is extracted from various sources is processed into a structure that meets the necessary requirements of the targeted business system.
Some ways that the data can be transformed include:
- Changing the data format
- Removing excessive duplications
- Eliminating inaccurate data and inconsistencies
Transformation is implemented through rules that you define for each data set. The automated program will treat the data and transform it accordingly.
Load
This is the phase in which your extracted data is loaded into the target system. In ETL protocols, this is the last step. Meanwhile, in ELT, this is the second step.
In ETL, the data is extracted and transformed before it is loaded into the target system—so you can directly proceed to data analysis in the target system.
In ELT, raw data is loaded into the target system before it is transformed—so you’ll have to wait for the data transformation to complete before you proceed to data analysis.
Differences Between ETL and ELT
There are key differences that affect the way the ETL and ELT protocols operate. These differences can cause a variance in the speed, data compatibility, costs, etc. Here’s a closer look at how these differences come into play.
| Property | ETL (Extraction, Transform, Load) | ELT (Extraction, Load, Transformation) |
|---|---|---|
| Data Compatibility | ETL is best for structured data that can be represented in tabular format. | ELT can handle all types of data, including images, unstructured documents, etc. |
| Transform and Load Location | Transformation happens before the loading process. Therefore, the data loaded in the target system has already been transformed. | Transformation happens after the loading process. Raw data is loaded onto the target system before transformation takes place. |
| Speed |
Since transformation occurs before the data is loaded into the target system, the system is slowed down. Once the loading is done, all the data is ready for quick access and analytics. |
ELT loads data to the target system first, allowing the system to utilize the full processing power of cloud data warehouses. |
| Security |
In order to comply with privacy rules and regulations, companies are required to protect personally identifiable information (PII). For ETL, developers need to come up with custom solutions to protect PII and monitor the data. |
ELT connects to the data warehouse, which directly provides security features like multi-factor authentication and granular access to protect PII. |
| Costs |
Analytics requirements in ETL need to be defined and structured before the process even begins. The time taken to set up these preliminary functions can be expensive. |
ELT needs fewer systems to process the data because the bulk of the load is handled by the data warehouse. |
ETL vs. ELT: Pros and Cons
Top ETL Pros for Enterprises
Fast Analysis
The intent of the ETL process is to make fully-transformed data sets available to the target system. Since the data present on the target system is ready for further utilization, data analysis can proceed without a hitch.
This is a great solution for enterprises that deal with large volumes of data that need to be readily available for deep-diving data analysis.
Flexibility of Environment
The transformed data can be rerouted to multiple target systems if required by your business. This is great for running simultaneous data analysis functions across your enterprise. ETL allows your business to create optimal pathways to channel the information to the required departments without compromising its integrity.
Compliance
By implementing preset rules for data transformation, you can ensure that the transformation can go beyond simple data integration. Aside from maintaining the integrity of your enterprise’s data, you can also implement protocols to ensure that your data adheres to regulatory requirements.
ETL Cons for Enterprises
Loading Speed
The resources allocated to carrying out the transformation in ETL are considerably higher than those allocated to ELT. This heavily taxes the working speed of the targeting system, causing an initial process slowdown.
A process slowdown is not ideal for businesses looking to make a quick transition from one system or process to another.
Rigidity of Workflow
Since the transformation process occurs before system transfer, protocols need to be set in place before the ETL process is initiated. This requires preset rules for data transformation with little to no room for mistakes or changes once the process is initiated.
Data Volume
The intermitted storage volume during the ETL process is limited. Large quantities of data can heavily task the system, so this process is ideal for lower data processing volumes.
ELT Pros
Flexibility of Data Formats
The transformation process takes place in the ELT system. Therefore, systems designed to handle ELT can support a wide variety of data formats. This allows for flexibility in data storage.
Transformation as Needed
There is less urgency for data transformation in ELT. You can load raw data and transform as needed for the sake of your intended process alone—data that is not involved in the processes can remain untouched.
High Availability of Data
The original formats of data will be readily available on the system, making it easier to conduct multiple varieties of transformation operations on the same data set.
Speed of Loading
Since loading and extraction occur almost simultaneously, the system is less taxed for resources. Pairing this with the benefit of high flexibility gives ELT systems an edge in cases where the data set is smaller and more manageable.
ELT Cons
Compliance
Since transformation occurs only as needed, compliance requirements aren’t strictly implemented. Raw data can become corrupted due to multiple iterations.
Newer Approach
The ELT process is relatively new. Therefore, it is not recommended when you are dealing with valuable information with a longer time stamp. When matured data is corrupted, it can be harder to recover.
Speed of Analysis
Since transformation takes place as needed, it can take longer for data to be ready for more detailed analysis.
ETL vs. ELT: Selecting the Best Data Management Strategy
Although it seems that ELT is the faster protocol, there are many cases in which ETL is more reliable for your enterprise. Let’s take a look at some scenarios where using ETL can be more beneficial for your business.
Legacy databases
ETL is more attuned to working with third-party data sources and legacy databases. You only need to transform the data once, after which it is readily available for all future analytics.
Experimentation
Data engineers can discover more through data experimentation—this can open doors to hidden avenues and new data-related business solutions. ETL allows data engineers and scientists to conduct investigative studies.
Complex analytics
Both ELT and ETL systems can be used in combination to carry out complex analytics on your chosen data set. Depending on the source and complexity of the procedure, utilizing both methods in tandem can optimize the process and increase performance.
Enter JIFFY.ai: The Revolutionary No-code Approach to Simplified ETL
JIFFY.ai is a no-code platform that allows large-scale businesses to optimize their operations with ease. No-code platforms like JIFFY.ai are revered for their ability to process data in bulk with accuracy and efficiency—making your ETL protocols accurate and faster without compromising data integrity.
By automating your ETL protocols with JIFFY.ai’s no-code platform, you can ensure that large data sets are effortlessly shepherded through scalable ETL processes—you can modify the way your systems operate and scale your automation to fit the size of your growing business.
Where does JIFFY.ai come into play? If you are a bank, financial institution, or media institution, you can process your large paper trails and vast quantities of data with ease using JIFFY's AI-powered, no-code platform.
JIFFY.ai also hosts a large subset of HyperApps that are geared towards automating repetitive business operations and data collection processes with ease—enabling your business to onboard and sustain more customers in the long run.
These HyperApps support a growing number of third-party integrations—so you can easily integrate all your data under one roof from multiple data pools without fear of compromising the integrity of your existing pool of data. With JIFFY.ai’s No-code ETL, you can make sure that your data is secure and stored safely in a cloud-based environment with ease of access and top-tier encryption.
