The database that runs a business and the database that answers its questions are usually two different systems. Day-to-day apps use an OLTP database built for fast, small transactions. Analysts work in a data warehouse (OLAP), where data from many sources is combined and organised for reporting.
You'll learn what a warehouse is and how data gets into it through ETL, then move on to data modeling: splitting data into fact tables (the numbers you measure) and dimension tables (the context, such as customer, product and date). The module finishes with the star schema, the layout behind most BI reports and dashboards.
A well-modeled warehouse makes every query simpler and every dashboard faster. In the next module, Data Visualization Principles, you'll learn how to turn that data into charts that people can read at a glance.
An introduction to data warehousing and BI: OLTP, OLAP, ETL, data marts, metadata, and warehouse architecture.
What extract, transform, load means, why companies build warehouses, and how OLTP and OLAP systems differ.
OLAP cubes, dimensions, facts and measures, and the main data warehouse schema types.
What fact and dimension tables are, and how to design a star schema using the free ERDPlus diagramming tool.