Every dashboard sits on top of data that had to be collected, cleaned and stored first. ETL (extract, transform, load) is the process that moves data out of source systems, reshapes it, and loads it somewhere analysts can use it. Even if you never build a pipeline yourself, understanding how they work helps you trust your numbers and talk to data engineers.
You'll start with ETL and ELT and how they differ. Then you'll look at where the data ends up, in data warehouses, data lakes and lakehouses, and at the main cloud data platforms such as Snowflake, BigQuery and Redshift. Finally you'll see how pipelines are scheduled and monitored with an orchestration tool like Apache Airflow.
This builds on the earlier modules on data modeling and warehousing and on data cleaning. In the final module, Data Storytelling & Stakeholders, you'll learn how to turn all of this work into insights people act on.
A beginner-friendly introduction to ETL and data pipelines, and why ETL is the backbone of a data warehouse.
A quick comparison of ETL (extract, transform, load) and ELT (extract, load, transform), and why both matter for data integration.
A simple explanation of what each one is and how they differ, with ETL and OLAP vs OLTP along the way.
A comparison of the leading cloud data platforms, and of lakehouses versus traditional data warehouses.
A beginner-friendly introduction to Airflow and how workflow orchestration schedules and runs data pipelines.