Data pipelines can be designed in many ways, but there are a few common patterns that are often used. In this article, I will discuss three of the most popular data pipeline design patterns: ETL, ELT, and CDC.
ETL (Extract, Transform, Load)
ETL is a traditional data pipeline design pattern that involves extracting data from a source system, transforming it into a desired format, and then loading it into a target system. ETL pipelines are typically implemented using a single data pipeline application.
1. Simple and easy to understand
2. Transformations can be applied to the data before it is loaded into the target system, which can improve the performance of the target system
1. Can be complex to implement for large and complex datasets
2. Can be difficult to maintain and update
ELT (Extract, Load, Transform)
ELT is a newer data pipeline design pattern that is becoming increasingly popular, especially for cloud-based data pipelines. ELT pipelines involve extracting data from a source system and loading it directly into a target system, without any upfront transformations. The transformations are then applied to the data in the target system.
1. Flexible and scalable
2. Easy to implement for large and complex datasets
3. Easy to maintain and update
1. Requires more computational resources than ETL
2. Transformations must be applied to the data in the target system, which can impact the performance of the target system