In this YouTube Short, we quickly explore the key differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data processing methods. ETL extracts data from various sources, transforms it into the desired format, and loads it into a data warehouse. ELT, on the other hand, loads the raw data into the destination system first and then transforms it within the system itself.
ETL is ideal when you need to ensure data quality and consistency before it reaches your data warehouse, making it great for structured data. ELT, however, is more flexible and scalable, allowing for faster data ingestion and processing, especially with unstructured data.
We'll highlight when to use each method based on your project needs, data volume, and infrastructure. Whether you’re handling small datasets or large-scale data pipelines, understanding ETL vs ELT helps you optimize your workflows and choose the right approach.
Stay tuned for a quick dive into the pros, cons, and real-world use cases of both!