With the development of data lakes and ELT, some analysts began predicting the market’s shift from ETL toward ELT. In this article, we look at two data integration methods and discuss whether ELT could replace ETL.
There are two data integration methods in data analytics: ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform). Essentially, both methods do the same thing – transfer data from one place to another. The main difference between the two methods is that ETL transforms data before storing it, while ELT transforms data after loading it to a storage.
Since both ETL and ELT include the same steps that are performed in a different order, the key question is: should we transform data before or after loading it in a data repository?
ETL is an older method of data integration. It is required for relational databases when data must be transformed into the relational format before ingestion. Think about a regular SQL Server. As part of the transformation process, data engineers map data from multiple sources, clean, enrich, and then store data in a warehouse.
Main advantages of ETL include:
- The structured nature of the OLAP data warehouse allows a quicker and more efficient data analysis for end users.
- Better data security and compliance. ETL allows a more secure way of transforming potentially sensitive information before putting it into a warehouse. This can be a key factor for companies regulated by GDPR and other security standards.
- Availability of resources and maturity. ETL has been around for 20+ years. There are many best practices, tools, and experienced engineers to help with the process.
As with everything else in life, there are pros and cons in ETL. Instead of focusing on disadvantages of the ETL process, let’s take a look at ELT and what benefits it provides over ETL.
Unlike ETL, ELT does not require transformation before the loading process. ELT takes raw data and moves it to a processing server for transformation to take place later. Data cleaning, enrichment, and transformation take place inside a warehouse.
The direct loading process results in a much faster data ingestion. As a matter of fact, during the ELT process data is loaded and transformed at the same time. With the increased amount of data, ELT can quickly process large amounts of different types of data. This makes ELT a faster process that is ideal for large datasets that require quick analysis.
Compared to ETL, ELT is a newer process that developed with the invention of cloud services. Because ELT is cloud-based, it uses automated solutions that are flexible and can be scaled out on demand. The cloud-based nature of ELT also leads to lower costs and maintenance, compatibility with data lakes, and processing of semi-structured and unstructured data sources.
Shift toward ELT
ELT is becoming more popular as more companies are adopting the cloud services and starting to use big data and machine learning. Leading cloud providers, such as Microsoft Azure, Snowflake, Amazon Redshift, and Google all offer their cloud infrastructure and services.
With data storage becoming cheaper, there is no limit on how much data can be stored. At the same time, modern cloud databases are becoming more powerful and can process large amounts of data effectively. This will lead to the adoption of ELT in the future. Companies that require fast analysis over large amounts of data will be moving toward ELT and data lakes.
Overall, ELT is providing more options in data integration. More options are always a good thing. However, the more traditional ETL process is here to stay as well. Companies that require a safer process for handling sensitive data, have legacy infrastructure, or use smaller datasets will keep using ETL. At the end of the day, why break something that isn’t broken.
Whether you need help with ETL or ELT, Centida is here to help. We offer a wide range of consulting services and have successfully delivered projects for large Fortune 500 companies and SMEs around the world.