So far you've seen where data is stored and how it is processed. This module is about connecting the pieces. A data pipeline is the chain of steps that takes data from its sources, cleans and reshapes it, and delivers it where it is needed, and it has to run again and again without anyone babysitting it.
You'll start with ETL (extract, transform, load) and its modern variant ELT, where raw data is loaded first and transformed inside the warehouse. Then you'll meet orchestration: tools like Apache Airflow that run each step in the right order, on a schedule, retry failures, and show you what went wrong when something breaks.
This is the glue of a data platform, and it ties the earlier modules together. Once you've finished it, move on to Cloud Platforms & the Lakehouse.
What ETL means, the three steps, and the tools used to carry them out, especially in data warehousing.
How an ETL pipeline works, how ETL compares with ELT, traditional versus cloud ETL, and the benefits and challenges.
A full course on Airflow: scheduling basics, DAGs, operators, sensors, hooks, and executors, ending with a data pipeline walkthrough.