ETL vs. ELT: Key Differences
Switchboard Jul 11
Table of Contents
ETL is the process by which data from different sources is unified. ‘E’ is the extraction of data from sources, ‘T’ is the transformation of data using rules, and ‘L’ is the loading of the unified data into its final destination.
Obviously, extraction must come first in this procedure, but who says that the data needs to be transformed before it is loaded? Some processes use an alternate method called ‘ELT’. So, how does this method work? And does it have any advantages over ETL?
What is ELT?
ELT stands for “extract, load, and transform” and essentially involves loading the extracted data into its final destination before applying transformation rules. That is, the phases are in a different order from ETL.
- Extract – Raw data is extracted from different sources, such as spreadsheets, social media, and databases. The whole datasets can be extracted, or some chunk according to predefined rules.
- Load – Rather than move large volumes of data into an interim location for rules to be applied, ELT transfers them directly into the target destination, which is usually a cloud-based data warehouse, such as Microsoft Azure, Google BigQuery, Snowflake, or Amazon Redshift.
- Transform – The final destination is where the data is cleansed, standardized, verified, sorted, formatted, and labeled.
What is the difference between ETL and ELT?
When considering ETL vs. ELT for a project, you should weigh the pros and cons. The difference between ETL and ELT is that ETL requires an intermediate location with processing engines in which to run transformations before the finished data can be loaded into the final destination. However, ELT uses processing engines in the target destination itself.
In some data pipelines, avoiding the use of an intermediate location, and efficiently transforming data within the final destination, can result in a streamlined data unification process. This is usually when the target is a cloud-based data warehouse, such as Google BigQuery, Amazon Redshift, Microsoft Azure, or Snowflake. These facilities allow raw data to be loaded and transformed as and when necessary, rather than as a static stage of a data pipeline.
In other data pipelines, ETL is the more suitable choice. This could be when the target destination requires the data to be in a particular format, because the ETL process transforms data prior to loading. Examples include situations where there’s a difference between supported data types in the sources and the destination, security restrictions prevent the storage of raw data in the target, or when the ability to process data in the final destination is limited.
Is ELT better than ETL?
When used with a suitable data pipeline, ELT can have a number of benefits. Removing the interim data location avoids steps which can take time and resources. This can speed up the process significantly, leading to a faster time-to-value.
The use of cloud-based data warehouses over those on-premises naturally enables easy, fast, and automatic scaling, depending on the load required. In contrast, locally-stored data warehouses require the purchase, installation, and configuration of hardware. This scalability also provides flexibility, since cloud-based systems allow transformations to be applied on-demand, so you can process raw data whenever and however needed.
However, the downside is that cloud-based warehouses don’t typically support datasets stored in other locations, so these must be moved into the cloud platform before processing. On the other hand, an ETL pipeline can deal with disparate locations without the need to load all of the datasets in their entirety before processing them. Another drawback of ELT is that the overhead for storing all of the raw data is usually higher.
As you can see, the use of ELT over ETL really depends on the particulars of your data pipeline and warehouse capabilities, but they are both classes of the same type of process. Ultimately, it doesn’t matter in which order you apply the transformation and loading, providing the process unifies your data properly so that it’s actionable in your data warehouse.
If you need help unifying your first or second-party data, we can help. Contact us to learn how.Schedule Demo
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