ETL vs ELT: 7 Major Differences Simplified
Want to know the difference between ETL vs ELT? The explosion of big data has put a huge strain on the data warehouse architecture. Organizations handle huge volumes of different types of data including social media, customer behavior, and big data. Organizations that have data warehouses use either extract, transform, load or extract, load, transform data integration methods.
Table of Contents
Table of Contents
- What is ETL?
- What is ELT?
- ETL vs ELT: How is ETL Different from the ELT Process?
- Which is Better: ETL or ELT?
- Limitations of ETL
- Limitations of ELT
What is ETL?
In this process, data is first extracted from the homogeneous/heterogeneous data sources and then deposited into a staging area. The data is then cleaned and transformed into the required format. Finally, the data is uploaded to the data warehouse.
This process is a good component of Online Analytical Processing (OLAP) based data warehouses like Microsoft Analysis Server, SAP NetWeaver, IBM Cognos, Jedox OLAP Server, etc. Since OLAP only accepts structured data, you must transform the data before loading it. Its traditional methods had a waiting period for the data to go through each phase. This made them time-consuming.
However, its modern solutions are much faster and easier.
As a part of this Data Transformation process, Data Mapping might be rendered as necessary to combine multiple data sources based on correlating information. This allows your Business Intelligence platform to analyze the information as a single, integrated unit. Here are a few details that will help you understand ETL better:
- ETL is an ongoing, continuous process with a well-defined workflow that can extract data from heterogeneous or homogenous data sources. Next, it can deposit the data to a staging area. From the staging area, the data undergoes a cleansing process, gets transformed and enriched, and is finally stored in a data warehouse.
- ETL needs required supervision, detailed planning, and coding by data developers and engineers since the old methods in hand-coding ETL transformations in Data Warehousing ate up a lot of time.
- Modern ETL solutions are faster and easier especially for Cloud-based SaaS platforms and Data Warehouses.
Advantages of ETL
One of the major advantages of this Data Integration process is the pre-structured nature of the OLAP data warehouse. Once the data has been transformed, it allows for a more efficient, speedier data analysis.
On the other hand, ELT is not suitable when a speedy data analysis is required. Another advantage of this process is compliance. Companies regulated by HIPAA, GDPR, or CCPA are required to encrypt specific data fields for privacy purposes.
This Data Integration process offers a more secure way of doing it because it transforms the data before moving it to the data warehouse. On the other hand, ELT requires users to upload sensitive data first. This data can show up in logs that SysAdmins can access.
What is ELT?
In this method, data can be loaded directly into the data warehouse after extracting it. The data doesn’t have to be moved to a temporary staging area. Data transformation is then done within the target database.
This process is used for data lakes that accept both structured and unstructured data. This means there is no need to transform the data before loading it.
In this process, data gets leveraged through a Data Warehouse to carry out basic transformations. This means that there is no need for any Data Staging. ELT utilizes cloud-based Data Warehousing solutions for all different types of data. The ELT process also works in tandem with Data Lakes. Here are a few details to help you understand the ELT process better:
- ELT is a relatively new technology that has been made possible by high-speed, cloud-based servers. Cloud-based Data Warehouses offer near-endless storage capabilities along with scalable processing power.
- With ELT, you can ingest just about anything as it becomes available. ELT when paired with a Data Lake allows you to ingest an ever-expanding pool of raw data instantaneously without any requirement to modify the data into a special format.
- ELT transforms only the data required for a specific analysis.
- It is also an important point to note that the systems and tools of ELT are still evolving, so they aren’t as reliable as ETL paired with an OLAP database.
Advantages of ELT
The primary benefit of this process is its flexibility and ease of storing new, unstructured data. It allows you to save any type of information, even if you haven’t transformed and structured it. It gives you immediate access to information whenever you want it.
Other advantages of this Data Integration process include:
- High Speed: It allows all data to get into the data warehouse immediately. Users can then get the data they need for analysis and transformation.
- Low Maintenance: This process is cloud-based, hence, it relies on automated solutions for maintenance.
- Quicker Loading: Since the transformation is done inside the warehouse, it reduces the time taken to load the data to its final destination.
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ETL vs ELT: How is ETL Different from the ELT Process?
ETL and ELT are different primarily in two ways. The location of data transformation, and how the data warehouses retain data differently.
- While ELT transforms data within the data warehouse itself, ETL transforms data on a separate processing server.
- While ELT transfers raw data directly to the data warehouse, ETL does not transmit raw data into the data warehouse.
A broad comparison between ETL vs ELT is explained in the table below.
|Order of the Process||Data is transformed and then loaded into the desired data warehouse||The data transformation takes place within the desired data warehouse. The data is first loaded and then transformed.|
|Key Focus||Loading data into databases after transforming data, masking data, normalizing, joining between tables in-flight.||Loading into Data Warehouses and then mapping schemas directly into the warehouse.|
|Privacy Compliance||Sensitive information can be edited before loading into the target system||Data is uploaded in its raw form without any sensitive details removed. Masking will be handled in the target system.|
|Maintenance Requirements||You have to manually understand the transformation logic and schema change.||The data warehouse takes care of the transformations required.|
|Latency||Higher latency||Lower latency|
|Analysis flexibility||Use cases and report models are well-defined.||Data can be added at any time with schema evolution. Analysts can build new views off the target warehouse.|
ETL vs ELT: 7 Key Differences
Here is a summary of all the differences between ETL vs ELT, the two Data Integration processes, listed in this article:
|Price||Costlier than its alternative and is suitable for small-to-medium-sized businesses.||Cheaper than its alternative and is scalable. It’s affordable to businesses of all sizes.|
|Data warehousing support||Can work with both on-premise and cloud-based data warehouses.||Only works with cloud-based data warehousing solutions.|
|Loading time||Loads data slowly due to the staging process.||Loads data faster.|
|Use Cases||This process can be used for use cases where data access need not be instant.||This process is preferred for companies that need instant data access and huge volumes of data.|
|Data Lake Compatibility||Typically not meant for Data Lakes. It simply transforms data for integration with a structured relational Data Warehouse system.||This process offers a pipeline for Data Lakes that can be used to ingest unstructured data.|
|Waiting Time to Load Information||The Loading time is greater than its alternative since it’s a multi-stage process.||Data Loading happens faster here since you don’t need to wait for transformations plus the data loads only once into the target data system. ELT provides a pipeline for Data Lakes to ingest unstructured data which can then be transformed on an as-needed basis.|
|Waiting Time to Perform Transformations||Transformations take a lot of time over here.||Transformations take place a lot faster since the transformations take place after loading and on an as-needed basis.|
Which is Better: ETL or ELT?
A new frontier in data integration has been opened up by cloud data warehouses, but a team’s demands must be taken into account when deciding between ETL and ELT.
Even though ELT has exciting new features, some teams will continue to utilize ETL because the approach makes sense for their specific deployment, whether or not it uses legacy infrastructure.
The data pipeline created by ETL makes it safer to handle sensitive data and to comply with regulations.
Both ETL and ELT have their own benefits and trade-offs. ETL is more flexible but also requires more resources to clean and transform data, ELT is efficient and faster but requires the target system to have more capabilities of handling raw data.
Which strategy to use can depend on the specific requirements of a given project and the capabilities of the target system.
Limitations of ETL
There are several limitations of ETL (Extract, Transform, Load) systems:
- Complexity: ETL systems can be complex to set up and maintain, particularly when integrating data from multiple sources with different structures and formats.
- Latency: The process of extracting, transforming, and loading data can introduce latency into the system. Depending on the size and complexity of the data, it can take a long time for data to be fully processed and loaded into the target system.
- Limited Scalability: ETL systems can be limited in their ability to handle large volumes of data, particularly if the data is coming from multiple sources. This can lead to performance bottlenecks and delays.
- Resource-Intensive: ETL requires a significant amount of resources (compute, storage and network bandwidth) to handle the data movement, transformation, and loading process, which can be costly and time-consuming.
- Error-Prone: ETL systems can be prone to errors, particularly when dealing with complex data transformations or when integrating data from multiple sources. This can lead to data inconsistencies and inaccuracies.
- Lack of Real-Time Data Processing: ETL systems often operate in batches, which can make it difficult to process data in real-time. This can be a limitation for organizations that require real-time data analysis.
That being said, ETL still a widely used technology in a various industry and it has a large ecosystem of tools and solutions, and the limitation can be mitigated by the right design, implementation, monitoring and testing, and choosing right tools.
Limitations of ELT
ELT (Extract, Load, Transform) is a data pipeline strategy that loads raw data into a target system and then performs the necessary transformations. However, there are some limitations to this approach:
- Complex Transformations: ELT relies on the target system to perform the data transformations, which can be complex and resource-intensive. This can be a limitation for systems that do not have the capability to handle such transformations.
- Data Quality: ELT loads raw data into the target system before performing any quality checks or cleaning, which can lead to issues such as incomplete or inaccurate data.
- Performance: Depending on the size and complexity of the data, ELT can be very resource-intensive and can negatively impact the performance of the target system.
- Security: ELT loads raw data into the target system, which can make it less secure than ETL because the data is not cleansed, transformed, or anonymized before loading, hence it requires a more stringent security measures.
- Lack of Flexibility: ELT is less flexible than ETL, which relies on a separate process to clean and transform data. This can be a limitation for organizations that have unique data requirements.
- Lack of Governance: ELT might lack the structure and control that ETL has, depending on the specific implementation, which makes it harder to maintain, audit and manage the data flows, it also harder to implement data lineage.
Again, ELT can be a great solution for specific use cases, and the limitation can be mitigated by the right design, implementation, monitoring and testing, and choosing the right tools. Additionally, most ELT systems are designed to integrate with other tools for data quality, data governance, and security.
In this ETL vs ELT article, you have learned the difference between the two key Data Integration processes and also when to use one process over the other.
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