Learn Data Warehousing From Scratch- From Solution Architect

Understand how the operational data store integrates data from multiple sources with minimal transformation and how the staging area acts as a placeholder before loading to the warehouse.

The course provides a quick recap of data warehousing concepts, including etl, staging areas, surrogate keys, fact and dimension tables, denormalization, and slowly changing dimensions.

Explore data warehouse methods by comparing Ralph Kimball's dimensional modeling with William's approach, and learn how a bottom-up design uses sources, staging, and ETL for decision making.

Select the business process and decide the data grain to design a dimensional model. Choose dimension tables and define fact tables with metrics and keys for high-performance data access.

Explore how fact tables anchor dimensional models, store numeric additive measurements, and link to dimensions through foreign keys, with sales order data examples.

Understand dimension tables in dimensional modeling, their attribute-driven structure, and their join to fact tables via foreign keys. See examples like product, city, time, and customer dimensions.

Explore a data warehousing case study for a multi-store grocery chain, detailing fact and dimension tables and data collection from point of sale and deliveries to optimize restocking and pricing.

The star schema centers a fact table surrounded by dimension tables promotion, store, product, sales rep, and customer, linking measures such as order quantity and extended amount via foreign keys.

Explore bus architecture in data warehousing by using conformed dimensions across multiple data marts and shared facts, while avoiding redundant account data.

Explore analytics and BI vendors across noSQL options, including key-value, document, and columnar databases, and examine how cloud and on-premise tools shape modern data warehousing.