When it comes to data processing, there are two main approaches: ETL and ELT. Many people are confused by the two terms. Both have their own advantages and disadvantages, so it's important to understand the key differences between them in order to choose the right approach for your needs.
ETL stands for Extract, Transform, Load. This is the traditional approach to data processing, in which data is extracted from various sources, transformed into a format that can be loaded into a target database, and then loaded into that database.
ELT stands for Extract, Load, Transform. This is a newer approach that has become possible due to scalability, the immediacy of it, and cloud data warehouses. In ELT, data is extracted from various sources and loaded into a target database. Once the data is in the database, it is transformed into the desired format.
9 key differences between ETL and ELT
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Data transformation:
In ETL, data transformation is done before data is loaded into the target system. In ELT, data transformation is done after data is loaded into the target system.
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Data loading:
In ETL, data is typically loaded into a staging area first, and then transformed and loaded into the target system. In ELT, data is directly loaded into the target system and then transformed.
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Data processing:
ETL is typically done in a batch process, while ELT can be done in real-time or batch.
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Flexibility:
ELT is generally more flexible than ETL since the transformation step can be easily changed without affecting the load process.
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Scalability:
ELT can be more scalable than ETL since they can auto-scale on cloud data warehouses. You could have an unlimited scale by spinning up max compute for a few minutes to take on the target system's transformations, then spin it down to baseline compute.
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Complexity:
ETL can be more complex than ELT since it typically involves multiple steps and tools.
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Skills required:
ETL requires skills in data modeling and transformation, while ELT requires skills in programming and database query languages.
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Cost:
ELT can be less expensive than ETL since it has the ability to spin up and down compute to help keep costs in check.
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Implementation time:
ELT can be faster to implement than ETL since the transformation step can be done after data is loaded into the target system.
Make more informed decisions
ETL and ELT are very different approaches to data processing, each with its own advantages and disadvantages. Hopefully this article has helped you understand the key differences between the two so that you can make an informed decision about which approach is right for your organization. If you’re interested in trying out ThoughtSpot’s self-service analytics capabilities, sign up for a free trial today.
