What is ELT?
ELT, short for Extract, Load, Transform, allows rapid data warehousing and real-time data analysis.
It is opposite to ETL, or Extract, Transform, Load, where complex transformation was prioritized over data warehousing, thereby compromising speed.
But is ELT superior to ETL in all cases? The answer is 'NO.' There are times when it's better to stick to the old school of thought.
In this article, we will explain what ELT is and when you can employ it for data analysis.
What is ELT in Data Integration?
ELT, as already mentioned, is an acronym for Extract, Load, Transform. It's a data integration methodology where data is first loaded into a data warehouse before being cleansed and processed. This reduces latency and improves speed.
With an ELT pipeline, all the data processing activities occur inside a data warehouse. Here is how ELT works:
Raw data is captured from multiple source databases. It may or may not be in the required format.
The data is loaded onto the target database after little to minimal processing.
The database takes over the data transformation process and alters the data as per requirement.
This methodology grew in prominence once cloud-based data warehouses became available and cost-effective. Companies no longer need to invest in expensive infrastructure and can buy computing resources cheaply.
Think Microsoft Azure, Snowflake, Amazon Redshift, and Google BigQuery as available options. All of these providers have in-built data processing. Further, names like NoSQL, CRMs, and Hadoop offer scalable data warehousing capabilities.
Although there are many benefits of ELT, it is a modern concept and still not used widely compared to ETL. To understand why, let's look briefly at ETL.
What is ETL?
ETL, an acronym for Extract, Transform, Load, is the traditional approach to data integration. Here, after data extraction, the data is processed in a secondary server before being loaded into a data warehouse or database.
ETL was the prominent methodology when data had to conform to the data regime of the database.
The databases won't just accept data in any format. Or the cost of having multi-format data was just too high.
Thus, companies refrained from capturing all the available data through APIs.
Moreover, there wasn't a huge amount of data at that time. We are talking about the 1970s up until 2014-15.
As per Statista, the data explosion took off around the 2016 mark. It's when we started consuming, capturing, and copying enormous amounts of data in an upward trajectory.
So even though ETL process was around as a concept and practice, it wasn't necessary.
If you are still using ETL within your company, these ETL tools can further streamline the process.
ETL vs ELT: Key Differences
The differences between ETL and ELT go beyond the position of T and L. In fact, in some cases, they resemble polar opposites. Here are the key differences between ETL and ELT:
1. Order of data integration
This is by far the biggest difference that every data architect and analyst should know.
ETL and ELT, at their cores, differ in the order of integrating data. In ELT solutions, the loading process comes first, with data transformation taking a back seat. In ETL, transformation is prioritized over data loading.
This grants the ELT methodology considerable computing speed.
You can analyze data in real-time as and when data is generated.
You can also get the full dataset, as it was generated, onto the warehouse.
This has pros and cons. But if having a full, uncontaminated dataset is your priority, ELT offers the best option.
2. Support for data lake
Also known as a data mart or data lakehouse, a data lake is a centralized repository where you can have sensitive data along with structured and unstructured data.
ETL doesn't have support for such endeavors. You're supposed to store data in a particular, uniform format.
Unstructured data is usually not accepted by ETL systems since data has to be transformed first.
But with ELT, you have the liberty to create a data lake. It supports unstructured data at scale and can handle it along with structured data, thus enabling better data management and business intelligence.
Another difference between the two data integration techniques is when and where data transformation is done. In ETL, the transformation part is done at a staging area. This is outside of the data warehouse.
Data, after it is extracted, is sent to the staging area before being incorporated into the warehouse.
With ELT, data transformation is done within the warehouse, and there's no need for a separate staging area for transforming data.
There may be some pre-processing involved. But it happens in the data pipeline. Then the major transformation happens within the data warehouse.
You may think since ELT servers handle large amounts of data, they must have high maintenance requirements.
But contrarily, the ETL servers have more maintenance calls, especially the on-premise servers. That's because of their undeviating nature. There are fixed tables and fixed timelines. Whenever there's a difference, the database runs into issues.
Moreover, there's a constant need to load the transformed data. The newer cloud-based ETL servers are a bit easier to handle.
With ELT servers, the transformation process is generally automated. Thus, there's less intervention necessary.
Also, the servers can handle different data types and data volumes, making them less complicated to maintain and work with.
Traditionally, companies were required to invest huge capital in setting up servers with high processing power on the premise.
So the hardware requirements for ETL data integration were high.
The newer cloud-based options take out that requirement. You can get started pretty easily.
But still, you may have to install some on-premise hardware if the business demands it.
ELT process, on the other hand, is inherently cloud-based. It grew in prominence with the availability of cloud computing. Also, the cloud-based servers are much more scalable and can handle large amounts of data.
Thus, there are no hardware requirements to get started with the ELT data integration approach.
But, again, if the process demands on-premise servers, you may have to install some.
Since ETL, traditionally, required to install servers on the premise, its costs run high.
You need to install and set up the servers. And then hire admins to look after them 24/7.
Thus, it's a high upfront cost undertaking. Unless you're considering cloud-based solutions, expect high costs.
With ELT, you can be up and running with little upfront cost.
Since SaaS cloud data warehouses have a pay-as-you-go pricing table that supports scalability, you pay small for little usage.
But you need to administer your cloud resources for efficient computing. Otherwise, the cost can easily go off the roofs.
ETL has been around for decades and has a structured framework and experienced talent pool behind it.
Thus, it's easier to implement. Except for the cost, ETL is the choice for many managers undertaking data integration and analysis projects.
ELT is a newer integration technique, and many of its things are still evolving.
The talent pool is smaller than ETL, so it's hard to find skilled data engineers. So, the implementation, just like any new technology/technique, stays challenging for ELT.
When to Use ELT instead of ETL?
There are cases when ELT reigns supreme over the traditional ETL methodology. Learning about these use cases will help you decide between ETL and ELT wisely. Here are the scenarios where ELT fits well as a data integration technique:
When data ingestion is a priority
There's a rapid rise in the generation of data. Some companies want to capture as much of the generated data as possible. With the traditional ETL approach, it wouldn't be possible. The data integration approach will transform much of the data before it becomes the database. So, what you'll be left with is processed data.
If data ingestion is your priority, go with ELT.
More and high-speed data ingestion is usually a priority for digital transformation projects. You're transforming your business around digital technologies and would need as much data as possible. In the optimization phase, you weed out the inefficient data and focus on the efficient ones.
So, the ELT approach is preferred over ETL for high data ingestion. Here are some amazing ELT tools that can help with the process.
When you have to deal with big data
With the rise in data availability, companies are turning to machine learning and artificial intelligence (ML and AI) for task automation. But both of these require a high amount of relevant data to work with.
ELT supports high-volume data to facilitate ML and AI projects. It is compatible with structured, non-structured, and semi-structured data. So, you can play with different data types.
When you're creating a data lake
Traditional data warehouses focus on a single type of data structure. The data may come from multiple sources in multiple formats but is stored only after processing. This reduces the value of data in some projects.
Data lake allows you to store large volumes of data in different formats. So, you can have structured and unstructured data in a centralized place. This is facilitated by the ELT approach.
But make sure to transform the data before you store it since data lakes offer little transformation capabilities.
When real-time data analytics is necessary
There's no latency with ELT. The data is pre-processed in milliseconds before being loaded onto the data warehouse or data lake. The analysts, thus, can look into the data in real time as they come. With this capability, data engineers can make quick decisions or even automate decision-making.
The traditional ETL approach is more suited for batch data processing. Once data has been collected at the source database, it is then transferred to the target database. This takes time, maybe even weeks, if that's the time horizon required to generate the batch of data.
When your budget is low
Not every data science team receives the same budget for their projects. The ELT data integration process makes sense when starting on a low budget. You buy the cloud resources and pay as per the usage. By optimizing for performance, you can keep the cloud costs lower.
With ETL, you can see a 2-10X rise in the cost. So, when on a tight budget, use ELT as your data integration technique.
Challenges with ELT
Not everything is good and bright with ELT. There are certain areas where the Extract-Load-Transform approach falls flat. As a data scientist, you must learn about these drawbacks.
Security and compliance
Arguably the biggest drawback with the ELT approach is with security and compliance.
When you accept data in any format and don't process the source data beforehand, it makes the repository vulnerable.
Because you don't know the authenticity and validity of the data, if such data go unchecked, it can easily corrupt the data warehouse.
Because of such reasons, ELT techniques don't usually comply with the regulators. They prefer companies to have reliable data. To stay compliant, you may consider some pre-processing before loading onto the warehouse. Encrypting or scanning the data for malicious content is an option.
ETL is still preferred for its security benefits.
More time for insight and data intelligence
The more untransformed data comes, the more time you'd spend transforming it.
So even though you may achieve real-time analysis, having the right insight into the data will be time-consuming. Again, the more variety in data, the more time delay.
Hard to find a skilled workforce
ELT is a new approach and is constantly evolving. Academia is catching up, but not at the desired pace. Therefore, finding the required workforce to work on ELT workflows is still difficult. While this problem is expected to subside in the coming years, it remains a challenge nonetheless.
Despite the difficulties, we strongly believe that ELT, as a data integration technique, is suited for many projects. EtLT (Exchange-transform/tweak-Load-Transform) is a subset of ELT that offers the best of both worlds. As the approach matures, you'll have more capabilities and opportunities.