ETL stands for Extract, Transform and Load while ELT stands for Extract, Load, and Transform.
ETL and ELT are acronyms for the process required to migrate and transform information from one data system to another. The functional difference between ETL and ELT relates to the order of the process:
- With ETL you extract data, transform it into a compatible structure, and load it into a target data warehouse system—so business intelligence tools can query and analyze it. ETL generally refers to the data transformation process required to use a data warehouse system, such as Amazon Redshift.
- With ELT you extract data, immediately load it into the target data lake system, and then transform the data—so business intelligence tools can query and analyze it. ELT generally refers to the data transformation process required to transform data already located in a data lake or warehouse.
Think of ETL vs ELT as an analogy for setting up a business conference or big event:
- Scenario 1: First you have ideas for how you want your event booth to look. Then you extract those ideas from your brain, and transform or change them based on your business needs. Some booths need TVs, some need projectors, some need swag, some need advanced lighting and sound, and some need all the above. Lastly, you load all of your equipment necessary into a truck and set-up at event location. This is ETL.
- Scenario 2: First you have ideas for how you want your event booth to look. Then you load all of those ideas into a truck and send them to the event location. Once you're at the conference location, you sift through every single idea you had for your event booth, and transform it on-site. This is ELT.
This basic difference between ETL and ELT is easy to explain. But understanding the big picture—i.e., the potential advantages of ETL vs. ELT—requires a deeper knowledge of how ETL works with data warehouses, and how ELT works with data lakes.
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Overview of ETL and ELT
The ETL and ELT are necessary in data science because information sources—whether they use a structured SQL database or an unstructured NoSQL database—will rarely use the same or compatible formats. Therefore, you have to clean, enrich, and transform your data sources before integrating them into an analyzable whole. That way, your business intelligence platform (like Looker, Chartio, Tableau, or QuickSight) can understand the data to derive insights from it.
Regardless of whether it's ETL or ELT, the data transformation/integration process involves the following three steps:
- Extract: Extraction refers to pulling the source data from the original database or data source. With ETL, the data goes into a temporary staging area. With ELT it goes immediately into a data lake storage system.
- Transform: Transformation refers to the process of changing the structure of the information, so it integrates with the target data system and the rest of the data in that system.
- Load: Loading refers to the process of depositing the information into a data storage system.
As we’ve already established, ETL and ELT perform these steps in a different order. So the question is: Should you transform your data before or after loading it into the data repository? To answer that, you need to understand ETL and ELT separately.
ETL Process in Detail
Online Analytical Processing (OLAP) data warehouses—whether they are cloud-based or onsite—need to work with relational SQL-based data structures. Therefore, any data you load into your OLAP data warehouse must be transformed into a relational format before the data warehouse can ingest it. As a part of this data transformation process, data mapping may also be necessary to join multiple data sources together based on correlating information (so your business intelligence platform can analyze the information as a single, integrated unit).
That’s why data warehouses require ETL—because the transformations must happens before the loading. Here are some details to understand about ETL:
- A continuous, ongoing process with a well-defined workflow: ETL first extracts data from homogeneous or heterogeneous data sources. Next, it deposits the data into a staging area. Then the data is cleansed, enriched, transformed, and stored in the data warehouse.
- Used to required detailed planning, supervision, and coding by data engineers and developers: The old-school methods of hand-coding ETL transformations in data warehousing took an enormous amount of time. Even after designing the process, it took time for the data to go through each stage when updating the data warehouse with new information.
- Modern ETL solutions are easier and faster: Modern ETL, especially for cloud-based data warehouses and cloud-based SaaS platforms, happens a lot faster. By using a cloud-based ETL solution, like Xplenty, users can instantly extract, transform, and load their data from diverse sources without having programming expertise.
The Biggest Advantages of ETL
One of the biggest advantages of ETL over ELT relates to the pre-structured nature of the OLAP data warehouse. After structuring/transforming the data, ETL allows for speedier, more efficient, more stable data analysis. In contrast, ELT isn't ideal when speedy analysis is desired.
Another significant advantage for ETL over ELT relates to compliance. Often companies regulated by GDPR, HIPAA, or CCPA need to remove, mask or encrypt specific data fields to protect the privacy of their clients. This could involve transforming emails to just the domain or removing the last part of an IP address. ETL provides a more secure way to perform these transformations because it transforms the data before putting it into the data warehouse. In contrast, ELT requires you to upload the sensitive data first. That causes it to show up in logs where SysAdmins can access to it. Also, using ELT to transform data could inadvertently violate the EU's GDPR compliance standards if non-compliant data leaves the EU when uploading it to a data lake. Ultimately, ETL reduces the risk of compliance violations because non-compliant data will never accidentally find its way into a data warehouse or reports.
Finally, as a data integration/transformation process, ETL has existed for over two decades, which means that there are many well-developed ETL tools and platforms available to assist with your data extraction, transformation, loading needs. Also, data engineers who are skilled and experienced at setting up ETL pipelines are easy to find.
ELT Process in Detail
"Data Lakes" are special kinds of data stores that—unlike OLAP data warehouses—accept any kind of structured or unstructured data. Data lakes don't require you to transform your data before loading it. You can immediately load any kind of raw information into a data lake, no matter the format or lack thereof.
Data transformation is still necessary before analyzing the data with a business intelligence platform. However, data cleansing, enrichment, and transformation occur after loading the data into the data lake. Here are some details to understand about ELT and data lakes:
- A new technology made possible by high-speed, cloud-based servers: ELT is a relatively new technology, made possible because of modern, cloud-based server technologies. Cloud-based data warehouses offer near-endless storage capabilities and scalable processing power. For example, platforms like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities.
- Ingest anything and everything as the data becomes available: ELT paired with a data lake lets you ingest an ever-expanding pool of raw data immediately, as it becomes available. There's no requirement to transform the data into a special format before saving it in the data lake.
- Transforms only the data you need: ELT transforms only the data required for a particular analysis. Although it can slow down the process of analyzing the data, it offers more flexibility—because you can transform the data in different ways on the fly to produce different types of metrics, forecasts and reports. Conversely, with ETL, the entire ETL pipeline—and the structure of the data in the OLAP warehouse—may require modification if the previously-decided structure doesn't allow for a new type of analysis.
- ELT is less-reliable than ETL: It’s important to note that the tools and systems of ELT are still evolving, so they're not as reliable as ETL paired with an OLAP database. Although it takes more effort to setup, ETL provides more accurate insights when dealing with massive pools of data. Also, ELT developers who know how to use ELT technology are more difficult to find than ETL developers.
The Biggest Advantages of ELT
The primary advantage of ELT over ETL relates to flexibility and ease of storing new, unstructured data. With ELT, you can save any type of information—even if you don’t have the time or ability to transform and structure it first—providing immediate access to all of your information whenever you want it. Furthermore, you don’t have to develop complex ETL processes before data ingests, and saves developers and BI analysts time when dealing with new information.
ETL vs. ELT Comparison
|Adoption of the technology and availability of tools and experts||ETL is a well-developed process used for over 20 years, and ETL experts are readily available.||
ELT is a new technology, so it can be difficult to locate experts and more challenging to develop an ELT pipeline compared to an ETL pipeline.
|Availability of data in the system||ETL only transforms and loads the data that you decide is necessary when creating the data warehouse and ETL process. Therefore, only this information will be available.||
ELT can load all data immediately, and users can determine later which data to transform and analyze.
|Can you add calculations?||Calculations will either replace existing columns, or you can append the dataset to push the calculation result to the target data system.||
ELT adds calculated columns directly to the existing dataset.
|Compatible with data lakes?||ETL is not normally a solution for data lakes. It transforms data for integration with a structured relational data warehouse system.||
ELT offers a pipeline for data lakes to ingest unstructured data. Then it transforms the data on an as-needed basis for analysis.
|Compliance||ETL can redact and remove sensitive information before putting it into the data warehouse or cloud server. This makes it easier to satisfy GDPR, HIPAA, and CCPA compliance standards. It also protects data from hacks and inadvertent exposure.||
ELT requires you to upload the data before redacting/removing sensitive information. This could violate GDPR, HIPAA, and CCPA standards. Sensitive information will be more vulnerable to hacks and inadvertent exposure. You could also violate some compliance standards if the cloud-server is in another country.
|Data size vs. complexity of transformations||ETL is best suited for dealing with smaller data sets that require complex transformations.||
ELT is best when dealing with massive amounts of structured and unstructured data.
|Data warehousing support||ETL works with cloud-based and onsite data warehouses. It requires a relational or structured data format.||
ELT works with cloud-based data warehousing solutions to support structured, unstructured, semi-structured, and raw data types.
|Hardware requirements||Cloud-based ETL platforms (like Xplenty) don't require special hardware. Legacy, onsite ETL processes have extensive and costly hardware requirements, but they are not as popular today.||
ELT processes are cloud-based and don't require special hardware.
|How are aggregations different?||Aggregation becomes more complicated as the dataset increases in size.||
As long as you have a powerful, cloud-based target data system, you can quickly process massive amounts of data.
|Implementation Complexity||ETL experts are easy to procure when building an ETL pipeline. Highly evolved ETL tools are also available to facilitate this process.||
As a new technology, the tools to implement an ELT solution are still evolving. Moreover, experts with the requisite ELT knowledge and skills can be difficult to find.
|Maintenance requirement||Automated, cloud-based ETL solutions, like Xplenty, require little maintenance. However, an onsite ETL solution that uses a physical server will require frequent maintenance.||
ELT is cloud-based and generally incorporates automated solutions, so very little maintenance is required.
|Order of the extract, transform, load process||Data transformations happen immediately after extraction within a staging area. After transformation, the data is loaded into the data warehouse.||
Data is extracted, then loaded into the target data system first. Only later is some of the data transformed on an “as-needed” basis for analytical purposes.
|Costs||Cloud-based SaaS ETL platforms that bill with a pay-per-session pricing model (such as Xplenty) offer flexible plans that start at approximately $100 and go up from there, depending on usage requirements. Meanwhile, an enterprise-level onsite ETL solution like Informatica could cost over $1 million a year!||
Cloud-based SaaS ELT platforms that bill with a pay-per-session pricing model offer flexible plans that start at approximately $100 and go up from there. One cost advantage of ELT is that you can load and save your data without incurring large fees, then apply transformations as needed. This can save money on initial costs if you just want to load and save information. However, financially strapped businesses may never be able to afford the processing power required to reap the full benefits of their data lake.
|Transformation process||Transformations happen within a staging area outside the data warehouse.||
Transformations happen inside the data system itself, and no staging area is required.
|Unstructured data support||ETL can be used to structure unstructured data, but it can’t be used to pass unstructured data into the target system.||
ELT is a solution for uploading unstructured data into a data lake and make unstructured data available to business intelligence systems.
|Waiting time to load information||ETL load times are longer than ELT because it's a multi-stage process: (1) data loads into the staging area, (2) transformations take place, (3) data loads into the data warehouse. Once the data is loaded, analysis of the information is faster than ELT.||
Data loading happens faster because there's no waiting for transformations and the data only loads one time into the target data system. However, analysis of the information is slower than ETL.
|Waiting time to perform transformations||Data transformations take more time initially because every piece of data requires transformation before loading. Also, as the size of the data system increases, transformations take longer. However, once transformed and in the system, analysis happens quickly and efficiently.||
Since transformations happen after loading, on an as-needed basis—and you transform only the data you need to analyze at the time—transformations happen a lot of faster. However, the need to continually transform data slows down the total time it takes for querying/analysis.
- ETL stands for Extract, Transform and Load while ELT stands for Extract, Load, and Transform.
- In ETL data flows from the data source, to staging, to the data destination.
- ELT lets the data destination do the transformation. No data staging is needed.
- ETL can help with data privacy and compliance, cleansing sensitive data before loading into the data destination, while ELT is simpler and for companies with minor data needs.
Fueling Your Data Mining
If you're suffering from any kind of data integration bottleneck, Xplenty's automated ETL platform offers a cloud-based, visual, and no-code interface that makes data integration and transformation less of a hassle. Check out Xplenty's hundreds of out-of-the-box integrations here. And if you have any questions, call our team to learn how Xplenty can solve your unique ETL challenges.