
Download the three Adventure Works backup databases (OLTP, data warehouse, and lightweight) from the official Microsoft site, then restore them using the SQL Server Management Studio.
Explore rowguid column and how a unique non clusters constraint supports replication, enabling unique identifiers across restored backups in the Adventure Works database crash course.
Explore the purchasing schema with the vendor as the central table, linking business entities and stores. Review ship methods, purchase order headers and details, and product vendor data.
Trace the origins of Adventure Works data from OLTP to the data warehouse. Learn how documentation, including Microsoft 2008 data warehouse references, clarifies structure amid outdated resources.
Explore how etl transfers oltp data into data warehouse tables to build the reseller data warehouse, tracing columns from the person phone, person address, sales store, and product lines.
Adventure Works Cycles is a fictional business developed and provided by Microsoft as a sample for learning business intelligence.
In this course, I will provide you with knowledge that could be beneficial to anyone diving into the world of business intelligence and is going to use the adventure works sample databases to learn.
First, this course will learn you how to download and install SQL/SSMS and restore database backup files if you are a total beginner when it comes to using Microsoft database programs. This course covers all three of the databases provided by Microsoft: The LT (Lite) version, the OLTP (Online Transaction Process) version, and the DW (Data Warehouse) version of the 2019 Edition of Adventure Works.
In the LT section of the course, bridge tables will be explained as well as rowguids.
In the OLTP section, the schema for the entirety of the OLTP database is covered and the relationships are explained.
The Data Warehouse section covers terms such as the snowflake and star architectures. This section is also going to dwell deeper into the principles of normalization and denormalization. The documentation will be explained as well and finally the analysis of the different tables featured in the Data Warehouse.