ETL vs ELT: 14 Major Differences

What is ETL?

ETL stands for Extract, Transform, and Load, commonly used in data warehousing and other data integration and procedures. Data transformation in ETL often occurs on a secondary processing server before loading it into the target data warehouse. It is a process of extracting data from one or more sources, transforming it into an appropriate format, and finally loading it into a destination system.

ETL Process

This is a crucial part of many data-driven organizations, allowing them to efficiently manage large amounts of data and ensure its consistency and accuracy. By following a well-defined ETL process, organizations can ensure that their data is accurate, up-to-date, and ready for analysis. After extraction, the next step is to transform data into a format well-suited for analysis.

The ETL process follows the following steps: 

• Data is extracted as it is assembled from different sources. This data may come from files, databases, or other sources and could be structured, semi-structured, or unstructured.
• After extraction, it is then transformed into a format well-suited for analysis. Data may be sorted, filtered, and cleansed during this step to ensure accuracy and correctness.
• Data mapping plays a crucial role in transforming and combining multiple data sources before loading them into a data warehouse.
• After transformation, the data is ready to be loaded into the target system. Depending on the requirements, this may be a data warehouse, a database, or another system.
• Once the data is loaded, it is available for analysis and reporting.

Benefits of ETL

ETL helps collect data from multiple sources, ensure data quality, and improve efficiency, allowing organizations to make better data-driven decisions. 

Some additional benefits ETL provides are:

• Increased Insight: Organizations can gain greater insights into their data by unifying and transforming data into a single data warehouse.
• Improved Data Security: ETL can guarantee that sensitive data is not disclosed by providing an additional layer of security. This can help protect the organization from data breaches and other security threats.

ETL vs. ELT: A Quick Comparison

ETL vs ELT: 14 Major Differences 

ETL vs ELT: Process Order 

ELT is a process in which data is extracted from its source, loaded into a target system, and then transformed into a usable format. Some benefits of ELT can be seen in the following cases:

• Where more processing power is needed to perform the transformations.
• When it is necessary to have access to the data in the target system before transforming it.
• It is more flexible as the transformation can be easily modified without re-extracting the data from the source system.

ELT allows data scientists to get involved with data at an earlier stage in the pipeline, removing bottlenecks that arose with ETL.

ELT is generally considered the more efficient approach since the data is loaded into the target system first and then goes through a transformation process in the target system itself.

ETL vs ELT: Maintenance 

With any technology, proper maintenance is required to ensure the ETL/ELT processes are running smoothly.

Some maintenance steps are mentioned below that can help ensure organizations that their ETL/ELT processes are running efficiently. 

• Monitoring is important to ensure that the ETL/ELT process is running as expected. 
• Scheduled maintenance includes checking for errors, and ensuring all components are up to date on a regular basis.
• Backups should be done to protect the ETL/ELT process from data loss. 
• Data quality: Data quality checks should be performed to ensure that data is consistent and meets the necessary quality standards. 

The maintenance required in the ELT process is similar to that of ETL in some respects, but there are also some differences. 

• With ELT, ensuring that the data transformations are correctly implemented is important. 
• It is essential to ensure that the target system can handle the data transformations. 

ELT requires similar maintenance as ETL but with some additional considerations. It is important to regularly monitor the system, the data loading process, and the data transformations to ensure that they are functioning correctly.

ETL vs ELT: Cost

• The cost of an ETL process is typically higher than that of an ELT process because it requires a powerful server and multiple databases to process and store the data. The cost savings from an ELT process are because it does not require the same level of processing power or database resources as an ETL process.
• Another essential cost consideration is the data egress cost. In general, ELT processes can result in lower data egress costs than ETL processes because the data is not transformed until it is already in the target data warehouse or data lake. This means that less data needs to be transferred out of the source systems, thus reducing egress costs. 

Check out more about data egress cost and how to save it here.

ETL vs ELT: Security

In ETL, data is extracted from source systems, transformed into the target format, and then loaded into the target system, usually a data warehouse. 

• This process is typically performed on a separate server, allowing for more control over data security. 
• The data transformation server can be configured to access only specific sources and can have appropriate access control and authentication measures in place.

In ELT, data is extracted directly from the source system and loaded into the target system, where it is then transformed. 

• This process is less secure than ETL, as the data is not transformed on a separate server but is exposed to the target system where the transformation is taking place. 
• Additionally, ELT may not provide the same level of control over the sources and authentication measures, as it completely relies on the target system to provide those controls. 

Overall, the security of ETL and ELT depends on the context in which the data is being processed. If more control and security are required, then ETL is clearly a better choice.

ETL vs ELT: Hardware Requirements

ETL requires a powerful and robust hardware setup to handle large volumes of data. The hardware requirements depend on the data volume and complexity of the extraction and transformation processes. 

ETL requires significant memory, CPU, and disk space to manage the data. Hardware requirements for ETL include 

• High-speed servers with a large RAM
• Increased capacity storage systems
• Specialized ETL tools

ELT process is inherently cloud-based so it does not require as much hardware as ETL The hardware requirements for ELT depend on the data volume and complexity of cloud data warehouses the extraction, and loading processes, or the ELT tool chosen by the organization.

ETL vs ELT: Support For Data Lake

Data lakes are cloud-based storage systems that can hold large amounts of structured, semi-structured, and both structured or unstructured data together.

Some benefits provided by data lakes are 

• They can store both structured and unstructured data, hence accommodating different types of data sources in data lakes is easy. 
• They are more cost-effective than traditional data warehouses since they don't require upfront investments in hardware and software. 

In the case of a data lake, ETL can be used to combine data from different sources, cleanse transform, and then load the data into the data lake for further processing and analysis. However, it requires a more complex setup, as it needs a separate ETL tool/server to transform and extract raw data before it is loaded into the data lake. ELT is better suited for data lake environments because it allows the raw data to be stored in the data lake and transformed on the fly instead of being transformed before being loaded. This allows for more flexibility and scalability in the data lake environment.

ETL vs ELT: Support for Data Warehouse

• ETL offers robust support for the data warehouse by ensuring that all the data is properly structured and formatted for optimal use in the warehouse. 
• ELT does not provide as much support for the data warehouse as ETL since it does not ensure that all of the data is properly structured and formatted before being loaded into the warehouse. Additional steps must be taken to ensure that all the data is properly organized and optimized for use in the warehouse.

If you deal with large volumes of data daily then ELT along with a data lake should be preferred in order to save costs and if you are dealing with less data then ETL with a cloud data lakes warehouse as the destination should be preferred.

ETL vs ELT: Performance 

Performance is an important criterion to ensure that the ETL/ELT processes can process large data volumes promptly. Poor performance can lead to data warehouses and applications being unable to produce timely and accurate information, or inconsistent or inaccurate data, which can directly impact business operations.

• In terms of performance, ELT is generally considered to be faster than ETL because the data transformation step is performed in parallel with the load step, using the computing power of the target system. This can be particularly beneficial when working with large volumes of data or when the target system is a powerful data warehouse optimized for data processing.

The ETL process is more secure, and ELT is faster. ETL is generally preferred for less data size, whereas ELT is used for organizations that produce high data volumes.

ETL vs ELT: Data movement

• In ETL, data is extracted from its source, transformed, and loaded into the target system. This process requires much data to be moved from the source to the target system.
• On the other hand, ELT is a less data-intensive process as data is extracted from its source, loaded into the target system, and then transformed. This process requires less data to be moved and processed than in ETL. In ELT systems since the data is already in the target system, the transformation process can be done in place, reducing the amount of data that needs to be transferred. This eliminates the need for moving the data between systems and reduces the risk of data corruption. However, the ELT process requires a more powerful target system to handle the increased data processing load.

Overall, ETL requires more data to be moved and processed than ELT, making it a more data-intensive process.

ETL vs ELT: Flexibility and Scalability

Scalability is an essential factor to consider when deciding between ETL/ELT processes. The two processes have different approaches for scaling up and each has its own pros and cons. 

ETL is a traditional data processing method and is considered more efficient and cost-effective than other methods. However, it is not as flexible or scalable as ELT as 

• ETL requires a lot of manual coding, so making changes or adding new components to the system is difficult. 
• The data transformation process can take a long time to complete, making it sometimes difficult to scale. 

ELT is a newer data processing method that involves extracting data from multiple sources, loading it into a data warehouse, and then transforming it. 

• ELT is more flexible and scalable than ETL, as it allows for changes and new components to be added without any hassles. 
• The data transformation process is typically much faster than in ETL, making it easier to scale. 

In conclusion, ELT is generally considered to be more scalable than ETL, as it is easier to make changes and add new components. However, both methods have their own benefits, and the choice of which to use depends on the specific needs and requirements of the organization.

ETL vs ELT: Support for Unstructured Data

Having proper data formats is essential for all organizations to get business intelligence and maximize their data's worth. Organizations should invest in the right ETL/ELT tools to get valuable insights into their business processes.

• In the ETL process, support for unstructured data is limited as the ETL processes are designed to handle structured data, with strict rules and predefined processes. This sometimes makes it difficult to accommodate unstructured data, which may come in various formats and types. As a result, ETL processes require extensive manual intervention and notable effort and skill to include unstructured data in the analysis.
• ELT processes on the other hand are better suited to support unstructured data as the process can easily accommodate a wide variety of data formats and can also incorporate manual intervention or other custom solutions according to the user's requirement. Additionally, the use of a staging area allows for the data to be manipulated before it is loaded into the target system, providing an additional layer of support for unstructured data.
  ETL vs ELT: Data Latency

Data latency is the time data moves from its source to its destination. In ETL/ELT processes, data latency will depend on the complexity of the transformations and the amount of data being processed. 

• In ETL, the data is first extracted from the source, then transformed, and then loaded into the destination. This process generally results in higher data latency, as the transformations must be completed before the data is loaded into the destination. If the transformation logic is complex then the ETL process can be time-consuming.
• In ELT, the data is extracted from the source, loaded into the destination directly, and then transformed. This process generally results in lower data latency, as the transformation step occurs after the data is loaded into the destination. 

Overall, ELT is considered to be more efficient than ETL in terms of data latency. However, it is important to consider the complexity of the transformations, as this can affect the overall data latency of both processes.

ETL vs ELT: Data Loss

Data loss in ETL/ ELT process is an important consideration when deciding which process order to follow for data transformation. 

• In ETL, data is extracted from different sources, transformed into the target system, and loaded into the target database. This process can cause data loss due to the transformation process as data that does not fit the target format or the transformation logic will be discarded, resulting in incomplete datasets and potentially inaccurate results sometimes. 
• In contrast, ELT moves all data into a target system before transforming it. This gives users more flexibility as there is no need to discard any information during the transformation process. However, because all raw data is loaded into a single database before being transformed, storage requirements may be higher and performance can suffer if not managed properly. 

Overall, ETL is more likely to result in some degree of data loss due to its rigid extraction and transformation steps while ELT offers more flexibility but may require additional resources to store and manage large amounts of raw data prior to transforming it.

ETL vs ELT: Maturity 

ETL relies on a traditional approach to data engineering, whereas ELT takes a more modern approach by extracting the data first and then loading it into the destination system before performing the transformation process. 

• ETL is typically more mature than ELT since it has been around for a longer time and is more widely used. Nowadays, organizations have more experience with , making them easier to implement and maintain. However, ELT can also offer significant benefits over ETL as it allows for greater scalability and flexibility when dealing with large datasets. 

Overall, both approaches have their benefits, but with time ELT is gaining more popularity due to its numerous benefits over ETL, especially for larger and more complex datasets.

The answer to which is better  ETL or ELT relies on the individual needs of an organization. ETL offers command over data transformation and can be a good fit for organizations with limited storage capabilities whereas ELT offers more flexibility in terms of how data transformations are performed and is more suitable for larger organizations with complex data pipelines.

To fulfill the needs of all kinds of organizations, Sprinkle Data is an excellent choice for businesses looking for an easy-to-use, comprehensive data pipeline and analytics platform. With over support of more than 100+ connectors, it is one of the most reliable tools that can cater to all needs. 

Frequently Asked Questions FAQs- ETL Vs ELT

Is ELT replacing ETL?

ETL is a well-established process that has been widely used for decades whereas ELT is recently developed that has become more popular in recent years due to the emergence of big data analytics technologies. Both processes serve their purposes and adopting any between the two will totally depend on the business requirements.

Is SQL needed in ETL? 

Yes, SQL is often used in ETL processes primarily in the transform step to transform the data into the desired format. SQL can also be used to perform queries on the data before or after loading it into a target system in both ETL and ELT approaches.

What is the difference between ETL and ELT? 

ETL is a process used to move data from one source to another. It involves extracting data from various sources, transforming the extracted data into a consistent format, and loading it into the target system whereas ELT is similar to ETL except that the transformation of the data happens after it is loaded into the target system.

Which is faster ETL or ELT?

ETL is considered to be slightly faster than ELT because it allows the transformation of data to take place before it is loaded into the destination system. ELT requires the data to be loaded first into a staging area before it can be transformed.

What tools does ELT use?

Some popular tools that use ELT are Sprinkle Data, Hevo, Streamsets, Blendo, and Fivetran.

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