Know the Difference Between ETL and ELT?
Within data management, there’s an ongoing debate between the extract-transform-load (ETL) and extract-load-transform (ELT) worlds of data integration and analytics. ETL and ELT are integral processes in data integration and have a distinctive variation in their approach. The ETL method moves data from the source to staging and into the data warehouse, allowing for intricate data transformations and cost-effectiveness. On the other hand, ELT uses the data warehouse’s capabilities for transformations, thus eliminating the need for data staging and facilitating potentially faster data processing.
The essential difference lies in the sequence of operations: ETL processes data before it enters the data warehouse. At the same time, ELT leverages the power of the data warehouse to transform data after it’s loaded. And as the digital landscape continues to evolve, it is vital to understand the critical differences between the two methodologies, which is equally essential in optimizing data transformation strategies.
However, the complexity of this topic extends beyond simple sequencing. From considerations on data privacy and compliance to cost-effectiveness, with this blog, I’ll try to delve into five critical differences between ETL and ELT while providing you with a comprehensive guide to making an informed decision that can be tailored to your data needs.
Before that, let’s try to understand ETL and ELT.
ETL: Extract, Transform, and Load
Used in data warehousing and integration, this process helps companies collect, process, and load data from various sources into one database or data warehouse. The main goal of the ETL process is to help companies consolidate their data and then analyze it from multiple sources, albeit consistently and meaningfully.
ELT: Extract, Load, and Transform
This is an alternative approach to data integration and processing, especially regarding data warehousing and data analytics. You see, what ELT does is flip the conventional ETL (Extract, Transform, Load) process on its head by rearranging the sequence of the data integration operations.
Now, let us look at the critical differences between the two.
ETL vs ELT: Main Differences
Despite the popularity both these approaches enjoy in the market, the fact remains that there persists a profound confusion between the two. This confusion is not just because they use the same alphabet in their names. So, let us begin with a quick look at what each of them stands for.
● Size or type of data sets: ETL is generally used for data sets with moderate to large volumes and is exceptionally well suited for processing and transforming structured and semi-structured data. ELT, on the other hand, is the better choice for companies that are dealing with data sets that are large and complex and include both structured and unstructured data. It must also be noted that ELT can accommodate a wide variety of data types and scales and quite effectively.
● Speed: Since ETL processes require the data to be transformed before it can be loaded into the target systems, ETL processes tend to take longer. As a result, a delay between data extraction and the availability of said data for further analysis can occur. Interestingly, ELT can be pretty fast regarding data availability because the data is quickly loaded into the target system and the transformations are performed within the data warehouse, storage platform, etc. This is why ELT is better suited for real-time or near-real-time analytics.
● Costs: Due to the need for additional infrastructure and processing power for the transformation phase, ETL processes can be costlier. Let us not forget that staging areas and intermediate storage can also add to the infrastructure costs. Then there is ELT, which has demonstrated the ability to be much more cost-effective, thanks to the fact that it uses the processing capabilities of the target storage system or data warehouse. But remember that with ELT, storage costs in the data warehouse may increase since raw data is stored along with the transformed data.
● Maintenance: ETL has proven to be substantially more complex to maintain since it involves the management of several systems. This is not the case with ELT, i.e., it is easier to maintain because it only needs management of the target system.
The above ETL vs ELT discussion demonstrates that these two processes are pretty individual. Hence, the choice between the two will depend on the company’s specific needs and priorities.
