ETL vs ELT – What’s the Difference? (Pros and Cons)
ETL vs ELT – What’s the Difference? (Pros and Cons). In this article, we discuss the differences between ETL and ELT and their advantages and disadvantages. Also we find out which tool is better used for different types of projects and what selection criteria are considered when deciding between ETL and ELT.
Nowadays, with organisations collecting huge amounts of data, it is important to use this data to improve the business value. In order to achieve this, many companies use data processing tools such as ETL and ELT.
These two popular data processing methods allow the integration and transformation of data from different sources. On one hand there is ETL – a traditional data processing method, which involves taking data from different sources. It then transforms it into a target form and loads it into the target system. Then there is ELT- a newer method that differs from ETL. Mainly, it involves loading data into the target system prior to transformation. Well, it allows the computing power of the target system to be used to perform the transformation.
What really is an ETL?
Firstly, ETL stands for Extract, Transform, Load. Simply put, a data processing process that involves extracting data from various sources, transforming it into the desired form and then loading. Data is loaded into a target system, such as a data warehouse or data store.
Albeit, the ETL process consists of three main stages:
Extract – extracting data from various sources, such as databases, text files, websites, or APIs.
Transform – it transforms the data to the desired form, including filtering, sorting, merging, or data format transformation.
Load – loads the transformed data into a target system. Usually a data warehouse or data store.
Use cases of ETL
Primarily, ETL is used to integrate data from various sources and transform it into a format useful for business analysis. The main purpose of using ETL is:
- Data integration – While ETL enables the integration of data from different sources, such as databases, text files, websites, or APIs, and brings them together in one place for easy analysis.
- Data processing and transformation – Enables data to be processed in a variety of ways, including filtering, sorting, merging, or data format transformation. This allows for consistent, qualitative and complete data.
- Searching and debugging – Allows you to search for and debug data errors, such as duplicates, missing values, or incorrect formatting.
- Automating data processing – ETL is also automated, allowing data from different sources to be processed regularly and quickly, which in turn allows for more timely and accurate data analysis.
Companies that use ETL?
Not only, many companies use ETL for their data integration needs. But also, here are a few examples of companies that use ETL:
- Netflix – Uses ETL to process and analyse data related to user behaviour, content consumption, and other metrics. They use a tool called Apache Flink to perform ETL processes.
- eBay – Uses ETL to integrate data from multiple sources into their data warehouse. They use a tool called Apache Kafka to perform ETL processes.
- Expedia – Expedia uses ETL to integrate data from various sources into their data warehouse. They use a tool called Talend to perform ETL processes.
- Verizon – Corporation uses ETL to integrate data from multiple systems into a data warehouse. They use a tool called Informatica PowerCenter to perform ETL processes.
- Walmart – The company uses ETL to integrate data from a variety of sources into its data warehouse, which is used for reporting and analysis. They use a tool called Teradata to perform ETL processes.
ETL Advantages
- Easier to maintain and track than hand coding.
- Ideal for data warehouse environments.
- Download different targets at the same time.
- Provide visual flow based on GUI (Graphical User Interface).
- Inbuilt error handling functionality.
- Generates higher revenue.
- Saves cost.
ETL Disadvantages
- Not suitable for near real time or on demand data access that requires fast response.
- Difficult to keep up with changing requirements.
- Takes months to put into practice.
- You must be a data oriented developer or database analyst to use it.
Up next with ETL vs ELT – What’s the Difference? we introduce ELT.
What is ELT?
All things considered, the Extract phase of ELT involves extracting data from various sources such as databases, applications, and files. After that, the Load phase involves loading the data into a staging area within the data warehouse. Finally, the Transform phase involves transforming the data into the desired format for analysis and reporting. As noted, this involves cleaning, filtering, joining, and aggregating the data.
Moreover, ELT has become more popular in recent years due to the growth of big data and cloud computing. With ELT, data is processed quicker and more efficient. Also, organizations more easily scale their data processing needs. As pointed, ELT also allows organizations to store large amounts of raw data in their data warehouse, which is useful for future analysis and usability.
Use cases of ELT ?
Unlike ETL, where data is transformed before being loaded into a target system, ELT involves loading data into the target system first, and then performing transformations on the data.
Here are some specific use cases for ELT:
- Data lake – Often used to load data into a data lake, which is a large repository of raw data used for analysis and reporting. The ELT process is used to extract data from various sources, load the data into a data lake, and then transform the data for analysis.
- Big data processing – ELT is often used to process large volumes of data in parallel. By loading data into a distributed computing environment like Apache Hadoop or Apache Spark, ELT processes transforms large volumes of data quickly and efficiently.
- Real time data processing – By loading data into a streaming data processing framework like Apache Kafka or Apache Flink, ELT processes transforms data in real-time as it is being generated.
- Cloud based data warehousing – Used to load data into cloud based data warehouses such as Amazon Redshift, Google BigQuery, and Snowflake. After, ELT processes are used to extract data from various sources, load the data into the data warehouse, and then transform the data as needed for analysis.
Companies that use ELT?
- LinkedIn – Uses ELT to process and analyse data related to user behaviour, bookings, and other metrics. They use a tool called Apache Spark to perform ELT processes.
- Uber – Uber applies ELT to process and analyse data related to ride bookings, driver behaviour and other metrics. They use a tool called Apache Hadoop to perform ELT processes.
- Twitter – The company uses an ELT system to process and analyse data related to user behaviour, tweets and other metrics. We run the ELT process using a tool called Apache Flink.
ELT Advantages
- Uses raw data that enables ELT to be highly flexible. This, combined with the fact that data is readily available, makes development easier.
- The process is easily restarted at the last successful step after errors are fixed without having to run the entire process from the beginning.
- For better scalability, the DWH ELT process uses an RDBMS engine.
- Source and target data are in the same database in ELT, so it retains all data in the RDBMS permanently.
- There’s better performance and data safety. Operates with high-end data devices like Hadoop clusters, cloud, or data appliances.
ELT Disadvantages
- Lack of modularity because of set based design and the lack of functionality and flexibility.
- The extra write operations slow things down. Sometimes ELT runs slowly because it stores each step in the process.
- Requires more system resources. Hence, ELT data storage requires more systems.
We have arrived to the main part of the article title ETL vs ELT – What’s the Difference? Please read on.
Comparison of ETL vs ELT - What is the difference?
Both tools, ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration processes used to move data from source systems into target systems, but they differ in the way data transformations are performed. Here are some of the most important differences between ETL and ELT
Data transformation
In ETL, data transformation occurs before the data is loaded into the target system. This approach requires a separate transformation engine and often increases processing time because the data must be transformed before loading. Conversely, the ELT process first loads data into the target system and then uses the processing power of the target system to transform the data. This approach reduces processing time, but also requires a target system capable of performing the data transformation.
Data volume
Here, ETL is better suited for processing large volumes of data, as the transformation engine is scaled to handle large data volumes. Comparatively, ELT, on the other hand, is better suited for processing smaller volumes of data, as the target system may not be able to handle large volumes of data and transformations at the same time.
Implementation
On one hand, with ETL the processes have been around for decades and there is a mature ecosystem of ETL tools and experts ready to assist with implementation.
On the other hand, the ELT process is a new approach, and the ecosystem of tools and experts needed to implement it is still growing and developing. All in all, not a mature solution.
Loading
Basically, ETL involves extracting data from source systems, transforming it into a format suitable for analysis, and loading it into a data warehouse. Conversely, in this approach, data is transformed before being loaded into the target system. As well as, ETL processes typically include data mapping, data cleansing, data aggregation, and data enrichment.
But, ELT, on the other hand, involves extracting data from source systems and loading it directly into a data lake or data warehouse without major transformations. Hence, conversion occurs after the data is loaded into the target system. With this approach, data is first loaded into the target system in raw format and then transformed using tools such as SQL or Hadoop. Especially, this approach provides greater flexibility in terms of data processing and analysis.
Size/type of data set
Especially ETL is best suited for handling small relational data sets that require complex transformations and are predetermined for analysis purposes.
Evidently, with ELT it handles data of any size and type and is well suited for handling both structured and unstructured big data. Because the entire data set is loaded, analysts choose at any time which data to transform and use for analysis.
Table Comparison of ETL vs ELT
| Table of comparison | ETL | ELT |
|---|---|---|
|
Cost |
ETL is prohibitively expensive for many SMBs. |
ELT benefits from a robust ecosystem of cloud platforms that offer significantly lower costs and multiple plan options for data storage and processing. |
|
Hardware |
Traditional local ETL processes require expensive hardware. Our new cloud ETL solution requires no hardware. |
The ELT process is cloud-based in nature, so no additional hardware is required. |
|
Compliance |
Better suited to GDPR, HIPAA, and CCPA compliance, in that it allows users to omit sensitive data before uploading it to the target system. |
The risk of personal data disclosure and non compliance with GDPR, HIPAA and CCPA standards is greater if all data is uploaded to the target system. |
Thank you for reading ETL vs ELT – What’s the Difference? We shall conclude the article now.
ETL vs ELT – What’s the Difference? Conclusion
In conclusion, both ETL and ELT approaches have their advantages and disadvantages. Simply, ETL is typically more suitable for structured data that requires significant transformation, while ELT is more appropriate for unstructured or semi structured data that requires less transformation. Ultimately, the choice between ETL and ELT depends on the specific needs of the organization, the complexity and volume of the data, and the desired level of transformation. It is important to carefully evaluate these factors and choose the approach that best meets the organization’s requirements for data processing, analysis, and reporting.
